Apr 2018: Our paper "Human Routine Change Detection using Bayesian Modelling" accepted at ICPR2018, to be presented in Beijing this August. It assigns an anomaly score to each data point in the time series, which can be used for generating alerts, monitoring through dashboards or connecting with your ticketing systems. Pygame + OpenCV Real-time Face Detection. Improved it's camera-based person detection & identification solutions, using state-of-the-art science and technologies to be used in Philips' clinical products in ICU and NICU. The real-time analytics application must simply be designed to leverage the full power of standard processors and memory. You only look once (YOLO) is a state-of-the-art, real-time object detection system. by the 3D LiDAR-based tracking. Model detection. Simply power your Arduino and open the android application. A: Please create an online repository like github or bitbucket to host your codes and models. It then streams and evaluates this data to produce statistics, detect the type of physical activity a patient is doing, and then detect or predict anomalies in a patient's heart rate. To detect an object, these systems take a 1. Try a live demo here. Dr Anil Bas is a Research Associate in the Department of Computer Engineering at Marmara University, Turkey. This way, we can visualize the detected face immediately and then update the emotions once the API call returns. Li: Single-Shot Scale-Aware Network for Real-Time Face Detection. His research interests include the detection and phasing of small variants in long read sequencing data. The human overseer can recognize and block all catastrophes. All of the general object detection models (as those mentioned above) fail to run in real time without a GPU. A set of 21 actions is selected for the challenge after the consultation with the expert medical professionals. Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun @ ICLR 2014 This model obtained 1st place in the 2013 ImageNet object localization challenge. If you are not a student, hope this article would be helpful when you cannot recall some models or algorithms. Ebola virus disease. CCS CONCEPTS • Human-centered computing → Ubiquitous and. Real-Time Hand Gesture Recognition (with source code) using Python In this work, we present a novel continuous technique for hand gesture recognition. GitHub is where people build software. and Intelligent Information Research Dept. C++ Python: ZED OpenPose: Uses ZED SDK and OpenPose skeleton detection to display real-time multi-person 3D pose of human bodies. hierarchically-structured human annotations is now pub-licly available [8], current evaluation methodologies are defined only for flat segmentations. The higher the mAp (minimum average precision), the better the model. Once we are ready with our hardware, code and Android Application its time for some action. A physics engine is computer software that provides an approximate simulation of certain physical systems, such as rigid body dynamics (including collision detection), soft body dynamics, and fluid dynamics, of use in the domains of computer graphics, video games and film. , series of images) plays an important role in various real life applications (e. If k <= 0, keep all the results. Our eye-blink detection scheme is developed based on the time difference between two open eye states. Updated May 2, 2020. We'll also add some features to detect eyes and mouth on multiple faces at the same time. One such application is. Real-time object detection plays a significant role in the field of computer vision. We develop our system by finding the greatest circle—pupil of an eye. Mixxx has now moved onto the VAMP Plugins set, which is much better, supports aubio and many other beat detection libraries. The created maps therefore carry geometric information, but no immediate semantic information. About I currently work at Google doing Machine Learning and Computer Vision research. Nanjing University, Nanjing, China September 2007 - June 2011 B. This is a practical but extremely challenging task, given the limitation that only images in a limited number from e-commerce sites are available in Perfect. Thank you so much in advance for your time. Presently, I am working on applications of both 2D and 3D synthetic data in tasks such as object detection, pose-estimation, semantic segm. This feature identifies objects, locations, activities, animal species, products, and more. Now I am writing real-time human detection program in Python2. Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras Yeqi Liu 1,2,3, Yingyi Chen 1,2,3, *, Huihui Yu 1,2,3, Xiaomin Fang 1,2,3, Chuanyang Gong 1,2,3 1. freenode-machinelearning. Zimu Zhou, Zheng Yang, Chenshu Wu, Yunhao Liu, and Lionel M. openpose computer-vision machine-learning multi-threading cpp cpp11 caffe opencv human-pose-estimation real-time deep-learning human-behavior-understanding cvpr-2017. For example, I applied Auto-ML for the same data set and accuracy improved from 57% to 66%. This paper presents a solution that makes use of a lightweight Convolutional Neural Network model, built according to a modification of the LeNet-5 architecture to perform age detection, for both males and females, in real-time. Viola et al [22] build an efcient moving person detector, using AdaBoost to train a chain of progressively more complex region rejection rules based on Haar-like wavelets and space-time differences. The perfect occasion to try people detection with the HOG algorithm, that can run on a CPU. On NVIDIA Jetson Nano, it achieves a low latency of 13ms (76fps) for online video recognition. Introduction. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The UTK-Face Large Scale Face Dataset was used to train and test the performance of the model in terms of predicting age. Leverage state-of-the-art Azure Machine Learning Anomaly Detection API to learn and react to anomalies from both historical and real-time data. He received a PhD in computer science from the University of Chicago under the supervision of Pedro Felzenszwalb in 2012. Feature Matching + Homography to find Objects. Inc, China tim. Cascade_human_detection. Considering challenging issues in multiple pedestrian detection, we present a real-time depth-based template matching people detector. Send a video stream into the container. In the first part we’ll learn how to extend last week’s tutorial to apply real-time object detection using deep learning and OpenCV to work with video streams and video files. Human pose estimation using OpenPose with TensorFlow (Part 1) Ale Solano. edu Chris English [email protected] Once GuardiCore detects a breach inside the data center, it provides Indicators of Compromise (IOC) to its Check Point Security Gateways using the STIX API, allowing security administrators to block future attacks in the data center and at the perimeter. This should be easy when overseeing a chatbot but difficult in domains with complex causal structure and time constraints (e. Originally, we used Caffe implementation of SSD-V2 for publication. I first try to apply object detection to my webcam stream. Project Description. Crone leads a long-standing research program using intracranial EEG to study human brain mechanisms of language, attention, and motor function. Dalal and B. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. important, since the real-time responders and rescue workers rely on the SA information to perform the effective and timely disaster relief to save human lives and reduce injury and property loss [2]–[4]. We develop our system by finding the greatest circle—pupil of an eye. Title: Towards Real-time Eyeblink Detection in The Wild: Dataset, Theory and Practices----- 10:30-11:00 Coffee break----- 11:00-12:00 Oral presentations 1. In this tutorial, we'll see how to create and launch a face detection algorithm in Python using OpenCV. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region. We’ll just clean the code up a bit and reformat it. In CVPR, 2017. New pull request. C++: CUDA Interoperability. in Electrical and Electronic Engineering (4. AU - Kurillo, Gregorij. We evaluate Bayesian object detection on a large synthetic and a real-world dataset and show how the estimated label uncertainty can be utilized to increase object detection performance under open-set conditions. 2%, using Fast-RCNN, improved it to 5. Real-time Human Detection based on Personness Estimation Kyuwon Kim1,2 q1. This system can prevent the loss of precious human lives and other resources. I have achieved very good results with this particular eye-tracker and the development SDK (C# only at this point in time) provides gaze and fixation event streams out of. 1discusses the algorithm and other relevant issues. Artificial Intelligence, Internet of Things. Flickr, Google, movies), advanced human ma-chine interfaces and automotive safety, among others. Below is a more complex example that utilises an SMI Red 500 eye-tracker and PyViewX. Human Detection using Faster RCN NAS COCO model (threshold = 0. Advanced Protip 4 hours 5,728. Furthermore, we implemented the k – Means algorithm to cluster the HOG feature vectors of the positively detected windows and determined the path followed by a person in the video. 1 Image sources for detection describes the algorithm to accomplish this. And the result everyone knows - face detection is now a default feature for almost every digital camera and cell phone in the market. Human-object interaction (HOI) detection strives to localize both the human and an object as well as the identification of complex interactions between them. Presently, I am working on applications of both 2D and 3D synthetic data in tasks such as object detection, pose-estimation, semantic segm. Our novel fully-convolutional pose formulation regresses 2D and 3D joint positions jointly in real time and does not. Human Computer Interaction(HCI) plays a vital role in day to day activities. Draper and J. It is the process in which algorithms are developed and trained to properly locate faces or objects (in object detection, a related system), in images. Face detection uses computer vision to extract information from images to recognize human faces. Use cases such as preventative maintenance, fraud prevention, fault detection, and monitoring can be found throughout numerous industries such as finance, IT, security, medical, energy, e-commerce, agriculture, and social media. /samples/pedestrian. We’re all well aware of what is happening around the globe and how the…. Object Detection Tutorial in TensorFlow: Real-Time Object Detection Last updated on May 22,2019 91. Awarded by Hong Kong PhD Fellowship. Our goal is to animate the facial expressions of a target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. This section is for you. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks 28 May 2017 | PR12, Paper, Machine Learning, CNN 이번 논문은 Microsoft Research에서 2015년 NIPS에 발표한 “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”입니다. In this paper, we present an efficient method to evaluate the eye location from facial images. , series of images) plays an important role in various real life applications (e. First of all, this approach is not the best but it is the fastest. The challenge evaluation was …. Some videos of this dataset were used for the experiments in our "Dymanic Classifer Selection" paper. Abstract: In environments where a camera is installed on a freely moving platform, e. One of the best implementations of facial landmark detection is by FacePlusPlus. Gurkirt Singh, Suman Saha and Fabio Cuzzolin Workshop on Anticipating Human Behavior, ECCVW, 2018 Online Real-time Multiple Spatiotemporal Action Localisation and Prediction Gurkirt Singh, Suman Saha, Michael Sapienza, Philip Torr and Fabio Cuzzolin International Conference on Computer Vision (ICCV), 2017. Caffe Classifier. Real-time face recognition and visualization via dlib and matplotlib - real_time_face. In CVPR, 2017. , series of images) plays an important role in various real life applications (e. All your code in one place. RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo and Lidar Graph-Based SLAM approach based on an incremental appearance-based loop closure detector. By providing high-performance shape detection capabilities this interface allows developers to run image analysis tasks on the local device. 2%, using Fast-RCNN, improved it to 5. 0 (161 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser. A: Please create an online repository like github or bitbucket to host your codes and models. Overfeat has been used by Apple for on-device face detection in iPhones: blogpost. detection_classes = self. /face_detection_ex faces/*. Prepare PASCAL VOC datasets and Prepare COCO datasets. This delay is the time taken to make the API call. Hardware components:. dissertation, “Efficient Nonparametric Kernel Density Estimation for Real-time Computer Vision”, Department of computer science, University of Maryland, College Park, 2002. In some way, we can say the design of residual blocks is inspired by V4 getting input directly from V1 in the human visual cortex system. This benchmark will come from the exact code we used for our laptop/desktop deep learning object detector from a few weeks ago. CNNs were not considered feasible for real time tra c sign detection due to. Discover the AmberBox Response Platform. Human Activity Recognition. Ross Girshick. Sinha, Pascal Fua Computer Vision and Pattern Recognition (CVPR), 2018. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, P. Currecntly i am working on Real time Human/People/fact detection in Video using OpenCV and GPU. If you stop and think about it, there is a lot of processing that goes on in our brains when we. 9% on COCO test-dev. 3 frames per second. The detection of anomalies in real-time streaming data has practical and significant applications across many industries. Let us jump right into running your own object detection model on a drone's video feed in real time. Hence, it is particularly well suited to real-time object detection tasks, such as object detection on streaming video. When combined together these methods can be used for super fast, real-time object detection on resource constrained devices (including the Raspberry Pi, smartphones, etc. Utilize this library in your machine learning projects, with both 2D and 3D real-time keypoint detection. Related Works Face detection has been an active research area since the development of computer vision, and many classical and deep learning approaches have been applied in this field. Going deeper. Real-Time Human Pose Tracking from Range Data (PDF, 2. This article will go through the most basic implementations of face detection including Cascade Classifiers, HOG windows and Deep Learning. This is a practical but extremely challenging task, given the limitation that only images in a limited number from e-commerce sites are available in Perfect. Recent publications Full list of publications. js GitHub repository. YOLO is a clever neural network for doing object detection in real-time. detection_classes = self. 4% on VOC2007 [4], but still has drawback in detecting smaller objects. In collaboration with Google Creative Lab, I’m excited to announce the release of a TensorFlow. There are tons of interesting problems to solve! One. freenode-machinelearning. Hence, it is particularly well suited to real-time object detection tasks, such as object detection on streaming video. Although numerous of efforts have already been paid, most of them focus on addressing the eyeblink detection problem under the constrained indoor conditions with the relative consistent subject. OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation. Barış Geçer Ph. Stefanos Zafeiriou from iBUG and Computer Vision and Deep Learning Scientist at Facesoft. Computer Vision for Human Detection By Ben Francis, ECE ‘18 _____ Introduction The field of computer vision has one general goal: to make computers and other machines “see” as we do. and the SSD model is available in the Tensorflow detection zoo. Improvement: Detect the boundary of the object as well: Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues,” D. Neveen Shlayan Zhekai Jin, Jialun Bao, and Rafi Mueen. This is a practical but extremely challenging task, given the limitation that only images in a limited number from e-commerce sites are available in Perfect. RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo and Lidar Graph-Based SLAM approach based on an incremental appearance-based loop closure detector. A: Please create an online repository like github or bitbucket to host your codes and models. and the SSD model is available in the Tensorflow detection zoo. This delay is the time taken to make the API call. (Best Paper Award on Human-Robot Interaction (HRI)) [ paper ] [ bib ] [ video ] [ dataset ] [ project ]. The foreground human figure will be detected using a self-organizing PCNN based segmentation. Let's mix it up with calib3d module to find objects in a. 1% mAP, outperforming a comparable state of the art Faster R-CNN model. The manual tasks, including fracture stage start/end labeling and ball pumpdown/seat event labeling, suffer from human bias and inconsistent errors, and can easily take up to days to finish. of Electronics and Electrical Engineering at Keio University. 332s OpenCV’s HOG detector should be much faster because its algorithm is much simpler than DPM, but how fast it is? real 1m55. Although not real-time, about 1 FPS, this work has been instrumental to the development of faster and more accurate features for pedestrian detection, which are used in the top per-forming methods in combination with SVM or Decision forests [5,12,26]. One of the main benefits of MIDAS is its ability to detect these anomalies in real time, at a speed many times greater than existing state-of-the-art models. You only look once (YOLO) is a state-of-the-art, real-time object detection system. Facial recognition using Raspberry Pi and OpenCV 3. However it is very natural to create a custom dataset of your choice for object detection tasks. I'll be doing a full blog post on the tradeoffs between speed and accuracy next week, but for reasonably size images (in the range of 400-600px) it can take anywhere between 0. Face detection in color images using PCA; Skin color detection under changing lighting conditions; Face detection and recognition in color images with a complex background (PhD Work from 2003) Computer Vision and Human Skin Colour (Moritz Stoerring’s PhD from 2004) Finding faces by motion: If you are able to use real-time video, you can use. I'm interested in human motion in general, with an emphasis on the dynamic simulation and control of virtual human characters. Itcher, and Orit Kliper-Gross. 0 (161 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Feature Matching + Homography to find Objects. Haar cascade based Human Detection Histograms of Oriented Gradients for Human Detection. Network behavior anomaly detection (NBAD) is the continuous monitoring of a proprietary network for unusual events or trends. He completed his PhD degree in computer science under the supervision of Dr William Smith in the CVPR Research Group at the University of York, UK in 2018. 861s user 1m54. It is aimed to improve the stand alone performance of these algorithms by using data acquired by mobile sensors. Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing, etc. “Hierarchical Evaluation of Segment Boundary Detection”, 16th International Society for Music Information Retrieval Conference, 2015. js GitHub repository. Visualize the weights of a 1-layer MNIST network developing in real-time CIFAR-10 weights Visualize the weights of a 1-layer CIFAR-10 network developing in real-time. Welcome to part 2 of the TensorFlow Object Detection API tutorial. More precise work is necessary to estimate the methylation level of each CpG site in the whole genome. This repository uses dlib's real-time pose estimation with OpenCV's affine transformation to try to make the eyes and bottom lip appear in the same location on each image. Lauer RT, Smith BT, Betz RR. This time the topic addressed was Semantic Segmentation in images, a task of the field of Computer Vision that consists in assigning a semantic label to every pixel in an image. Hand gesture recognition is very significant for human-computer interaction. In this tutorial, we'll see how to create and launch a face detection algorithm in Python using OpenCV. GitHub is where people build software. The performance of six existing deep learning architectures were compared for the task of detection of mango fruit in images of tree canopies. Estimate human poses in real-time (PoseNet). The time it takes per image is really dependent on how fast your system is. IEEE International Conference on Computer Vision (ICCV), 2015. It will enable numerous exciting deep learning researches for clinical tasks. All videos in the dataset are in HD format (1920x1080). It has shown promising applica-tions for real-time object detection in videos, and player-. Simply place the phone in our mobile holder and sit in. Title: Towards Real-time Eyeblink Detection in The Wild: Dataset, Theory and Practices----- 10:30-11:00 Coffee break----- 11:00-12:00 Oral presentations 1. edu Chris English [email protected] Imperva Bot Management gives you the most visibility and control over human, good bot, and bad bot traffic. Multi-modal (audio, video, caption) approach to learn a knowledge base of non-verbal language. See LICENSE for additional details. org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month. I'm currently working on renewing my amateur radio license. All your code in one place. Real-world Use Cases of Anomaly Detection in Graphs Put simply, anomaly detection is the practice of finding patterns or outliers that deviate from what you expect to see in a dataset. George , SL. SSD achieves 75. Advanced Protip 4 hours 5,728. So make sure the image is in grayscale. This could theoretically accomplish any job that a human being could perform. Van-Thuan Pham. The project focuses on a real-time robust human detection and tracking system for video surveillance that can be used in varying situations. We introduce a new deep learning architecture that naturally extends the single-shot 2D object detection paradigm to 6D object pose estimation. We evaluate the framework using a new 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The Anomaly Detection API is used in the 'Try It Now' experience and the deployed solution. Note: A smaller variant of their model called "Fast YOLO" claims to achieve 155 FPS on a GPU. Human preadipocyte cell lines were obtained from superficial fat depots in the neck of patients undergoing cervical spine surgery and immortalized with stable transfection of human telomerase. This delay is the time taken to make the API call. , Kansas State University , and GTU Robocon Team , where I worked with Hiren Galiyawala , Kenil Shah , Yash Khandhediya. In the previous post, I showed you how to implement pre-trained VGG16 model, and have it recognize my testing images. Keypoints are the same thing as interest points. Henry (Yuhao) Zhou. Project status: Published/In Market. If you want to experiment this on a web browser, check out the TensorFlow. We are doing so by fulfilling our mission to accelerate the human side of software development. #N#PoseNet can detect human figures in images and videos using either a single-pose algorithm. Real-time object detection with deep learning and OpenCV. Representation • Bounding-box • Face Detection, Human Detection, Vehicle Detection, Text Detection, general Object Detection • Point • Semantic segmentation (will be discussed in next week). Real Time Face Tracking and Pose Estimation Using an Adaptive Correlation Filter for Human-Robot Interaction Vo Duc My and Andreas Zell Abstract—In this paper, we present a real time algorithm for mobile robots to track human faces and estimate face poses accurately, even when humans move freely and far away from. 3Tbps DDoS attack pummeled GitHub for 15-20 minutes. For example, if your model is supposed to detect the ball on the football field, you probably don’t need to use the elongated (1:3, 3:1 and so) boxes, but the close-to-central-symmetric boxes will do. Dr Anil Bas is a Research Associate in the Department of Computer Engineering at Marmara University, Turkey. OpenCV is a highly optimized library with focus on real-time applications. The model we’ll be using in this blog post is a Caffe version of the original TensorFlow implementation by Howard et al. It has shown promising applica-tions for real-time object detection in videos, and player-. RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo and Lidar Graph-Based SLAM approach based on an incremental appearance-based loop closure detector. We consider the problem of estimating human pose and trajectory by an aerial robot with a monocular camera in near real time. The UTK-Face Large Scale Face Dataset was used to train and test the performance of the model in terms of predicting age. Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien @ IMWUT June 2019- Ubicomp 2019 Workshop [email protected] Self-supervised Learning Workshop ICML 2019 We've created a Transformation Prediction Network, a self-supervised neural network for representation learning from sensory data that does not require access to any form of semantic labels, e. An ideal line segment detection algorithm would process any image regardless of its origin, orientation or size, and produce robust and accurate line segments, i. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or. Bolme Yui Man Lui Bruce A. Processing the EVAL dataset: MP4 Video (15. , topic modeling, word embedding) InfoVis and CHI: Geospatial visualization (multi-scale navigation and interaction, context preservation) Text visualization. Can anyone suggest a method for real-time pitch tracking or. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region. The prototype system includes a Xtion Pro live sensor, a laptop for processing and transducing the data, and a set of headphones for providing feedback to the user. Research interests: 3D/4D Computer Vision, Scene Understanding, Segmentation, Multi-view Performance Capture and Human Computer Interaction which include robust computer vision to model and understand real world scenes. This is a mostly auto-generated list of review articles on machine learning and artificial intelligence that are on arXiv. It shows a frame time of approximately 150–170 milliseconds per frame (equivalent to 6. More precise work is necessary to estimate the methylation level of each CpG site in the whole genome. to create our real time TensorFlow custom object detection by using it. Pedestrian detection systems have become a crucial topic due to their contribution to numerous applications, such as surveillance, self-driving cars, track Real-time pedestrian detection using a support vector machine and stixel information - IEEE Conference Publication. We have released the source code behind this demo in our main GitHub repository in demos/sphere. 0047 seconds. On a Pascal Titan X it processes images at 30 FPS and has a mAP of 57. Our tracker is signi cantly faster than previous methods that use neural networks for tracking, which are typ-ically very slow to run and not practical for real-time applications. You can run this program on them and see the detections by executing the following command:. An implementation of our work (Online Real-time Multiple Spatiotemporal Action Localisation and Prediction) published in ICCV 2017. Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer IEEE Intelligent Vehicles Symposium (IV) , 2019 (Oral Presentation) Featured in Bosch Global Research. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks 28 May 2017 | PR12, Paper, Machine Learning, CNN 이번 논문은 Microsoft Research에서 2015년 NIPS에 발표한 “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”입니다. Publications. “Salient Object Subitizing”. It is aimed to improve the stand alone performance of these algorithms by using data acquired by mobile sensors. in Computer Science and Technology, Supervisor:Prof. time print ("Elapsed Time:", end_time-start_time) for (x, y, w, h) in. These can be in real time from a video camera or from photographs. Our solution works well in never-seen. detection_classes = self. This induces a class imbalance however when you consider the nature of the problem, it seems reasonable to keep the same data bias with your ML model since negative instances are more expectable in a real-life scenario. So make sure the image is in grayscale. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. Thereby, the templates are projections of the model at varying perspectives. We report the optimization of circular consensus sequencing (CCS) to improve the accuracy of single-molecule real-time (SMRT) sequencing (PacBio) and generate highly accurate (99. In this article, we develop a real-time mobile phone-based gaze tracking and eye-blink detection system on Android platform. As a result, the dimen-. View Article PubMed/NCBI Google Scholar 22. Although the pioneering at-tempts, namely You Look Only Once (YOLO) [24] and Single Shot Detector (SSD) [22], illustrate the ability of real-time processing, they tend to sacrifice. Real-Time Collision Detection, by Christer Ericson, Morgan Kaufmann, 2005. This will be accomplished using the highly efficient VideoStream class discussed in this tutorial. However, these methods need large amount of samples for training. Real-time Human Detection based on Personness Estimation Kyuwon Kim1,2 q1. aspects of the data. Object detection/segmentation is a first step to many interesting problems! While not perfect, you can assume you have bounding boxes for your visual tasks! Examples: scene graph prediction, dense captioning, medical imaging features. The Project focuses on a real time robust human detection and tracking system for video surveillance which can be used in varying environments. Feb 12, 2018 • Share / Permalink. It is the process in which algorithms are developed and trained to properly locate faces or objects (in object detection, a related system), in images. Use our microphone to record speech 3. This face-boxer. Building a real-time big data pipeline (part 1: Kafka) 8 minute read. Real-time Human Detection based on Personness Estimation Kyuwon Kim1,2 q1. Representation • Bounding-box • Face Detection, Human Detection, Vehicle Detection, Text Detection, general Object Detection • Point • Semantic segmentation (Instance Segmentation). supplementary / code. detection_graph. from cv import * def. Realtime does not imply fast, only that there is a strict maximum response time for a a given event. More than 50 million people use GitHub to discover, fork, and contribute to over 100 million projects. Feb 12, 2018 • Share / Permalink. AU - Kurillo, Gregorij. Real-time semantic image segmentation with DeepLab in Tensorflow A couple of hours ago, I came across the new blog of Google Research. We evaluate the framework using a new 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. GitHub Gist: instantly share code, notes, and snippets. To determine the ability of FINDER to detect potentially unsafe restaurants, we introduced FINDER into two local health. freenode-machinelearning. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Skeleton Detection. Feature Matching + Homography to find Objects. Generalized Hierarchical Matching for Sub-category Aware Object Classification (VOC2012 classification task winner). Request Text Detection for a Video on Google Cloud Storage videointelligence/apiv1" "github. Knowledge of gaze direction can give valuable information regarding users point of attention. Recent publications Full list of publications. When multiplied with the image dimensions, pixel coordinates can be obtained. On the other hand, you can improve the accuracy. This dissertation deals with the means of improving the current state of human detection, tracking and segmentation based on learning scene-specific information in a video. This is a ROS package developed for object detection in camera images. What Is Face Detection? Face detection is a type of computer vision technology that is able to identify people's faces within digital images. The Project focuses on a real time robust human detection and tracking system for video surveillance which can be used in varying environments. T1 - Real-time human pose detection and tracking for tele-rehabilitation in virtual reality. An AGI can be obtained by many means, but one of them it to take inspiration from Human Intelligence. Dec 2017: Pytorch implementation of our work on Online Real-time action Detection is available on GitHub. 2% using HDR images Course Projects, CMU. 18 Jun 2017 » You Only Look Once: Unified, Real-Time Object Detection 12 Jun 2017 » Convolutional Neural Networks for Sentence Classification 28 May 2017 » Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. So I assume that this is a movie-plot threat. When the application is also user-friendly, it puts the power of real-time business intelligence directly into the hands of business users. , it works for outdoor scenes, community videos, and low quality commodity RGB cameras. In the first part of today's post on object detection using deep learning we'll discuss Single Shot Detectors and MobileNets. This article will go through the most basic implementations of face detection including Cascade Classifiers, HOG windows and Deep Learning. This framework, being able to operate in real-time on 2001 hardware, was partially devoted to human face detection. The UTK-Face Large Scale Face Dataset was used to train and test the performance of the model in terms of predicting age. Billy Rowell is a Senior Bioinformatics Scientist on the Bioinformatics Applications Team at PacBio. Ross Beveridge Computer Science Department Colorado State University Fort Collins, CO 80521, USA {bolme,lui,draper,ross}@cs. (Best Paper Award on Human-Robot Interaction (HRI)) [ paper ] [ bib ] [ video ] [ dataset ] [ project ]. By combining my previous tegra-cam. OpenPose represents the first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) on single images. In its large version, it can detect thousands of object types in a quick and efficient. Facial Emotion Recognition in Real Time Dan Duncan [email protected] Automatically mitigate 100% of OWASP Automated Threats without imposing friction on legitimate users. aspects of the data. Pitch detection in Python. These time series are basically network measurements coming every 10 minutes, and some of them are periodic (i. StreamCast: Fast and Online Mining of Power Grid Time Sequences. {"code":200,"message":"ok","data":{"html":". This way, we can visualize the detected face immediately and then update the emotions once the API call returns. First introduced in 2015 by Redmon et al. An example of an IC board with defects. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class events. See LICENSE for additional details. Implemented Fast-RCNN and Scale-aware Fast-RCNN networks for pedestrian detection Achieved a state-of-the-art miss rate of 7. To accelerate detection, a single-stage framework is investigated, where the phase of object proposal generation is discarded. One such application is. 1 Image sources for detection describes the algorithm to accomplish this. This delay is the time taken to make the API call. CCS CONCEPTS • Human-centered computing → Ubiquitous and. Cha, Moving Human Area Detection using Depth Segmentation, In Summer Conference on Korea Society of Computer and Information, 2012. What Is Face Detection? Face detection is a type of computer vision technology that is able to identify people's faces within digital images. Billy Rowell is a Senior Bioinformatics Scientist on the Bioinformatics Applications Team at PacBio. Continuation of arXiv:1608. 861s user 1m54. Deformable Parts Models [17] have shown success on the pedestrian detection task [33,40]. Paper, Slide. Task description This task evaluated performance of the sound event detection systems in multisource conditions similar to our everyday life, where the sound sources are rarely heard in isolation. , no false detections, in a very short amount of time, preferably in real-time. by the 3D LiDAR-based tracking. Human Mesh Recovery (HMR): End-to-end adversarial learning of human pose and shape. One of these reasons is the variation of human size in the video frame. On Wednesday, at about 12:15 pm EST, 1. detection_graph. In this study, we developed an automated calamity detection system using deep learning, which can predict disasters in real-time and send an alert message. , allowing us to estimate human poses in the same framework. The detection of anomalies in real-time streaming data has practical and significant applications across many industries. pmid:11482364. By analyzing only key frames instead of all frames, the recognition speed is improved. Introduction. Taut is a full stack clone of the popular messaging service Slack with real time messaging, channels, and full front and back end user authentication. Pose Machine: Estimating Articulated Pose from Images (slide by Wei Yang) [Mmlab seminar 2016] deep learning for human pose estimation (slide by Wei Yang) Human Pose Estimation by Deep Learning (slide by Wei Yang). More and more organizations are looking to leverage real-time data analytics to avoid a reactive approach by catching fraud after Fight it in it has occurred. The assumption here is that it is possible (necessary) to identify the person from the point clouds by looking at local regions each time. Introduction. Intelligent Human Computer Interaction (IHCI), 2012 4th International Conference on A Drowsiness Detection Scheme Based on Fusion of Voice and Vision Cues A. If you are not a student, hope this article would be helpful when you cannot recall some models or algorithms. YOLO: Real-Time Object Detection. Research interests: 3D/4D Computer Vision, Scene Understanding, Segmentation, Multi-view Performance Capture and Human Computer Interaction which include robust computer vision to model and understand real world scenes. I first try to apply object detection to my webcam stream. 65 contributors. MultiPoseNet can jointly handle person detection, keypoint detection, person segmentation and pose estimation problems The novel assignment method is implemented by the Pose Residual Network (PRN) which receives keypoint and person detections, and produces accurate poses by assigning keypoints to person instances. So, it's perfect for real-time face recognition using a camera. It will enable numerous exciting deep learning researches for clinical tasks. It was developed for an Orbbec Astra camera DRGB (Depth-RGB), uses the OpenNI2 driver, it also works with Asus Xtion and Prime sense. [8] Tensorbox : Tensorbox is an implementation of GoogLeNet-Overfeat with two independent FC layers for softmax and regression. This offers a privacy advantage over offloading computation to a remote system. 이 논문에서는 이미지의 bounding box를 실시간으로 검출할 수 있는 YOLO라는 새로운 네트워크 모델을 제안합니다. 4% on VOC2007 [4], but still has drawback in detecting smaller objects. The face-boxer. com Abstract We present a new framework to robustly and efficiently detect abandoned and removed objects in complex environments for real-time video surveillance. handong1587's blog. The project focuses on a real-time robust human detection and tracking system for video surveillance that can be used in varying situations. 3D point clouds from depth camera, 3D marker positions from Vicon motion capture system, and estimated true body skeleton (3D joint positions): main_eccv_data. My research lies at the intersection of deep-learning, computer vision, computer graphics and robotics. Salekin, S. Jim walks through creating an object detection model, tagging regions in images that show the objects to be detected, then he builds a simple Xamarin app to use this to annotate a picture from his. GitHub is where people build software. State of the art Terminator. These improvements both reduce the number of region proposals and accelerate the test-time operation of the model to near real-time with then state-of-the-art performance. If you compile Darknet with CUDA then it can process images waaay faster than you can type them in. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or. A model based on Scalable Object Detection using Deep Neural Networks to localize and track people/cars/potted plants and many others in the camera preview in real-time. (2020) Pattern of early human-to-human transmission of Wuhan 2019-nCoV. Overfeat has been used by Apple for on-device face detection in iPhones: blogpost. The first chapter is about static face detection: at the end of the chapter we will be able to construct applications capable of detecting faces on static images or pictures. Spatiotemporal data (e. , Kansas State University , and GTU Robocon Team , where I worked with Hiren Galiyawala , Kenil Shah , Yash Khandhediya. Although numerous of efforts have already been paid, most of them focus on addressing the eyeblink detection problem under the constrained indoor conditions with the relative consistent subject. get_tensor_by_name('detection_classes:0') Hei @KeitelDOG how to find out the index of the class? @hndr91 you will find it in the data directory of tensorflow models in oddl directory of the User. impractical for detection problems due to its speed (at 14s per image, it would result in a very delayed detection). [data]: a coma separated set of numbers, with the time-series amplitude of the signal, each device uses a different precision to identify the electrical potential captured from the brain: integers in the case of MW & MU or real numbers in the case of EP & IN. Teams should open their source codes and models before challenge result notification deadline (August 9, PST time). Ebola virus disease. A graphic user interface (GUI) allows users to perform tasks interactively through controls like switches and sliders. edu Gautam Shine [email protected] RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo and Lidar Graph-Based SLAM approach based on an incremental appearance-based loop closure detector. Syncsort and Real-Time Fraud Detection. Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing, etc. Two weeks ago I discussed how to detect eye blinks in video streams using facial landmarks. node iand edge eat time t S ie(t) Power change: S ie(t) = S ie(t) S ie(t 1) X i(t) Sensor vector for scenario iat time t c Anomalousness threshold parameter ~ i(t) Median of sensor iat time t ˙~ i(t) Inter-quartile range of sensor iat time t a i(t) Sensor-level anomalousness for sensor iat time t A(t) Total anomalousness at. Object-based localization method with neural networks using YOLO (real-time object detector) to localize objects in the camera frame. In the previous post, I showed you how to implement pre-trained VGG16 model, and have it recognize my testing images. Improved it's camera-based person detection & identification solutions, using state-of-the-art science and technologies to be used in Philips' clinical products in ICU and NICU. Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks Jonathan Tompson, Murphy Stein, Ken Perlin, Yann LeCun SIGGRAPH 2014 A novel method for real-time pose recovery of markerless complex articulable objects from a single depth image. pedestrian detection would have immediate and far reaching impact to applications such as surveillance, robotics, assis-tive technology for the visually impaired, content based in-dexing (e. OpenPose is an open source library of real-time, multi-person keypoint detection for human bodies, faces, hands, and foot estimation. On test data sets of naturally occurring events curated by human experts, our system exhibits roughly 60% fewer detection failures than the currently deployed automated system, SEL3, that forms part of the International Monitoring System. A graph database ensures that relationship-oriented queries are conducted in real time, so your anti-fraud team has a chance to strike first. If you'd like to collaborate on something interesting, feel free to get in touch!. jpg This face detector is made using the now classic Histogram of Oriented Gradients (HOG) feature combined with a linear classifier, an image pyramid, and sliding window detection scheme. More Examples. Introduction Detection and localization are among the most useful func-. The manual tasks, including fracture stage start/end labeling and ball pumpdown/seat event labeling, suffer from human bias and inconsistent errors, and can easily take up to days to finish. We have developed a prototype system and evaluated it with real-world traces including normal traffic and several real-world botnet traces. , their paper, You Only Look Once: Unified, Real-Time Object Detection, details an object detector capable of super real-time object detection, obtaining 45 FPS on a GPU. The result of answering machine detection. For those not satisfied with the demo and have more understanding how the model is built and trained. Real Time Object Recognition (Part 2) 6 minute read So here we are again, in the second part of my Real time Object Recognition project. Test Dataset: INRIAPerson Test image's size: From 480 x 680 (pixels) to 1260 x 930 (pixels) Detection's Time: 0 - 30 (s), correspond to image's size. for real-time applications. Activity notifications. Human pose estimation using OpenPose with TensorFlow (Part 1) Ale Solano. IEEE International Conference on Computer Vision (ICCV), 2015. Real-time GCC-NMF Blind Speech Separation and Enhancement is a software that eases the audio interaction through the separation and enhancement of sound sources. Sanghoon Lee Yonsei University, Seoul, Korea. Therefore, there has been a lot of interest in learning-based 3D reconstruction in recent years. In this tutorial, we'll see how to create and launch a face detection algorithm in Python using OpenCV. We evaluate the framework using a new 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. Omama, Akshay Varshney, Mohammad Samar Ansari, Rishabh Sharma 下一篇 Human vs. Here, we demonstrate an approach to identify anomalously. A web-based video conferencing application tracks a pose of user’s skeleton by running a machine learning model, which allows for real-time human pose estimation, such as to recognize her gesture and body language. Human Computer Interaction(HCI) plays a vital role in day to day activities. detectMultiScale (gray_frame) end_time = time. Salekin, S. YOLO Training. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. 1 , which previously omitted a significant amount of fish. Draper and J. Malik, Proc. CNNs were not considered feasible for real time tra c sign detection due to. PPDM is released under the MIT license. During the pandemic time, I stay at home and play with this facemesh model. This could theoretically accomplish any job that a human being could perform. Most of the recent approaches use either the shape information only and ignore the role of color information or vice versa. Real-Time Object Detection for ROS. Managing GitHub Packages. However, for a dramatic interpretation of your query, I suggest "Daemon" and "Freedom TM" by Daniel Suarez. The loop closure detector uses a bag-of-words approach to determinate how likely a new image comes from a previous location or a new location. This post provides a step-by-step tutorial to run an object detection model on a drone's live video feed. Constructing an object detection dataset will cost more time, yet it will result most likely in a better model. Network behavior anomaly detection (NBAD) is the continuous monitoring of a proprietary network for unusual events or trends. There have been several research attempts to design effective and appropriately priced fire detection systems with varying degrees of success. Stefanos Zafeiriou from iBUG and Computer Vision and Deep Learning Scientist at Facesoft. We’re all well aware of what is happening around the globe and how the…. George , SL. Realtime does not imply fast, only that there is a strict maximum response time for a a given event. As the plane remains in the scene over time, ARKit refines its estimate of position and extent. In this tutorial, we're going to cover how to adapt the sample code from the API's github repo to apply object detection to. Effects of weather-related social distancing on city-scale transmission of respiratory viruses. 9% on COCO test-dev. 3D point clouds from depth camera, 3D marker positions from Vicon motion capture system, and estimated true body skeleton (3D joint positions): main_eccv_data. We’re all well aware of what is happening around the globe and how the…. International Journal of Computer Vision (IJCV), 2017. Originally, we used Caffe implementation of SSD-V2 for publication. Chris Fotache is an AI researcher with CYNET. Dec 2017: Pytorch implementation of Two stream InceptionV3 trained for action recognition using Kinetics dataset is available. Test Dataset: INRIAPerson Test image's size: From 480 x 680 (pixels) to 1260 x 930 (pixels) Detection's Time: 0 - 30 (s), correspond to image's size. His research focus is on generative image modeling. Human detection is the first step of the human posture analysis. 17 Nov 2018 in Data on Paper. REsCUE: A framework for REal-time feedback on behavioral CUEs using multimodal anomaly detection. The state-of-the-art methods can be categorized into two main types: one-stage methods and two stage-methods. The initial program output of this project is shown in Fig. An accurate and efficient eye detector is essential for many computer vision applications. js released the MediaPipe Facemesh model in March, it is a lightweight machine learning pipeline predicting 486 3D facial landmarks to infer the approximate surface geometry of a human face. Problems like fraud and cybercrime are highly complex and involve densely connected data from many sources. Explosive growth — All the named GAN variants cumulatively since 2014. student at Imperial College London, supervised by Dr. Skeleton Detection. C++ Cuda CMake Shell. Simply power your Arduino and open the android application. However, the plus point of the ssd_incpetion model was that it ran almost 3 times faster than the faster_rcnn model on simulator and almost 5–6 times faster on the real images. “Hierarchical Evaluation of Segment Boundary Detection”, 16th International Society for Music Information Retrieval Conference, 2015. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. In the first part we’ll learn how to extend last week’s tutorial to apply real-time object detection using deep learning and OpenCV to work with video streams and video files. I have achieved very good results with this particular eye-tracker and the development SDK (C# only at this point in time) provides gaze and fixation event streams out of. From a technical point of view, then, a suitable online surgeon action detection system must be able to: (1) locate and classify multiple action instances in real time; (2) connect the detection associated bounding boxes. Recommended citation: Tal Hassner, Yossi. 11% and a recall of 41. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region. Region proposal methods typically rely on inexpensive features and economical inference schemes. The annotations include instance segmentations for object belonging to 80 categories, stuff segmentations for 91 categories, keypoint annotations for person instances, and five image captions per image. Although the pioneering at-tempts, namely You Look Only Once (YOLO) [24] and Single Shot Detector (SSD) [22], illustrate the ability of real-time processing, they tend to sacrifice. We propose a real-time framework for the classification of eye gaze direction and estimation of eye accessing cues. The time scale examined by SDS is roughly an order of magnitude shorter than the limits of sensitivity for previous methods that study hard sweeps. ros2_object_analytics is a group of ROS2 packages for real-time object detection, localization and tracking. Real-time Lane Marker Detection Using Template Matching with RGB-D Camera This paper addresses the problem of lane detection which is fundamental 06/05/2018 ∙ by Cong Hoang Quach, et al. Representation • Bounding-box • Face Detection, Human Detection, Vehicle Detection, Text Detection, general Object Detection • Point • Semantic segmentation (will be discussed in next week). VNect: real-time 3D human pose estimation with a single RGB camera (SIGGRAPH 2017 Presentation) - Duration: 19:47. Jim walks through creating an object detection model, tagging regions in images that show the objects to be detected, then he builds a simple Xamarin app to use this to annotate a picture from his. Ask Question 6. Simplified version of real-time audio scoring for goal detection - realtime_detection. 0 (161 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. m On my laptop, it reports: real 8m19. by the 3D LiDAR-based tracking. However it is very natural to create a custom dataset of your choice for object detection tasks. The Project focuses on a real time robust human detection and tracking system for video surveillance which can be used in varying environments. This way, we can visualize the detected face immediately and then update the emotions once the API call returns. Then, how can we revisit the object detection problem. Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object. Plane detection results vary over time—when a plane is first detected, its position and extent may be inaccurate. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. In my free time, I contribute as an embedded systems developer to BReATHE, a sensor-based real-time pediatric asthma evaluation platform at the LA PRISMS Center. Flickr, Google, movies), advanced human ma-chine interfaces and automotive safety, among others. I’m full of curiosity. The feature vectors are created by segmenting song(s) and extracting time and and frequency domain features from them, such as chromagrams, chord degree and interval classification. Code for our CVPR 2020 paper "PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection". Research I want to build intelligent AI agents with human-level vision capabilities. However, the plus point of the ssd_incpetion model was that it ran almost 3 times faster than the faster_rcnn model on simulator and almost 5–6 times faster on the real images. Draper and J. We shall train a customized YOLO Neural Network using Darknet with the Japanese Food100 dataset! The Food Watcher will become the most advanced AI which can recognize the common food in real-time. Main network. Mamta Sharma and a team at ICRISAT are using Microsoft cloud computing and AI together with IoT sensors to help with real-time monitoring of small farms in developing countries and provide pest diagnosis and farm and market advice to farmers through an AI-supported mobile application that displays personalized prediction results and. And the result everyone knows - face detection is now a default feature for almost every digital camera and cell phone in the market. GitHub has built the system of record for code and has emerged as the de facto standard for software development in today’s world. In this paper, we propose a real-time action recognition method that achieves state-of-the-art accuracy on both single-action and multi-action recognition. Training code and test code on HICO-Det dataset. YOLO: Real-Time Object Detection. There are several algorithms for object detection, with YOLO and SSD among the most popular. detectMultiScale (gray_frame) end_time = time. , allowing us to estimate human poses in the same framework. This is due to two rea- sons: (1) Extremely high cost for constructing large scale dataset with exhaustive logo instance bounding box labelling [6]; (2) Lack-. To detect an object, these systems take a 1. The R code files that were used for this analysis can be found on GitHub. is an Associate Professor of Neurology and director of the Cognitive Neurophysiology and Brain-Machine Interface Laboratory. Real-time mode allows unlimited number of frames within a 60 seconds period There is a 90 seconds time out after these 20 seconds during which no requests can be made Additional requests can be made after this time out has finished. Real-Time Food Detection. REsCUE: A framework for REal-time feedback on behavioral CUEs using multimodal anomaly detection. Jim walks through creating an object detection model, tagging regions in images that show the objects to be detected, then he builds a simple Xamarin app to use this to annotate a picture from his. It works with normal webcam too, in the case of background is smooth and white. real-time testing time for our application, we use the auxil-iary information at the training stage only. OpenPose represents the first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) on single images. Let’s time it on INRIA dataset (288 images). Inference time comparison between the 3 available pose estimation libraries: OpenPose, Alpha-Pose (fast Pytorch version), and Mask R. com Abstract We present a new framework to robustly and efficiently detect abandoned and removed objects in complex environments for real-time video surveillance. StreamCast: Fast and Online Mining of Power Grid Time Sequences. The participants used 1 hour and 32 minutes of audio in 24 recordings to train their systems. 9ajzyw7b3hsh, 2fm5mtz8jlas8, i0hqv7ojhpr, mwvg9cbqjxe37, 84rgsv7jzt5o33, dm1xt18xlar, uto3igmctja31zy, uxxcxoeehtuz, rmf24n079ema, h6xq64ysp19t7, 4qpk404rjugxpdw, okew4bn7eqla, 0yljzo7kb6vt, r8awuj4sh26wpk, zryldg0lsid8u, ojbj76o4w9muj, hf48pfy9823u2x, a4oyjnanr8520, wjftyownjds4bs7, vz00awjlmpmm, zmut2o827f00s34, n06m3abbf1y, ed3j8iv6sut8, 8u7v74eqdmolc, 6w1aqbt13f5ru, qupr4qs7wmr