Counting in High-Density Crowd Videos

Slides:

Advertisements

Similar presentations

Human Identity Recognition in Aerial Images Omar Oreifej Ramin Mehran Mubarak Shah CVPR 2010, June Computer Vision Lab of UCF.

Advertisements

Introduction To Tracking

Supervised by Dr. Hau-San WONG Prepared by Kam-fung YU ( )

Activity Recognition Aneeq Zia. Agenda What is activity recognition Typical methods used for action recognition “Evaluation of local spatio-temporal features.

Kiyoshi Irie, Tomoaki Yoshida, and Masahiro Tomono 2011 IEEE International Conference on Robotics and Automation Shanghai International Conference Center.

2.Our Framework 2.1. Enforcing Temporal Consistency by Post Processing  Human Detection from Yang and Ramanan [1] Articulated Pose Estimation using Flexible.

Tracking Multiple Occluding People by Localizing on Multiple Scene Planes Professor ：王聖智教授 Student ：周節.

Object Inter-Camera Tracking with non- overlapping views: A new dynamic approach Trevor Montcalm Bubaker Boufama.

Adviser ： Ming-Yuan Shieh Student ID ： M Student ： Chung-Chieh Lien VIDEO OBJECT SEGMENTATION AND ITS SALIENT MOTION DETECTION USING ADAPTIVE BACKGROUND.

Tracking Multiple Occluding People by Localizing on Multiple Scene Planes Saad M. Khan and Mubarak Shah, PAMI, VOL. 31, NO. 3, MARCH 2009, Donguk Seo

Discriminative Segment Annotation in Weakly Labeled Video Kevin Tang, Rahul Sukthankar Appeared in CVPR 2013 (Oral)

A KLT-Based Approach for Occlusion Handling in Human Tracking Chenyuan Zhang, Jiu Xu, Axel Beaugendre and Satoshi Goto 2012 Picture Coding Symposium.

Oklahoma State University Generative Graphical Models for Maneuvering Object Tracking and Dynamics Analysis Xin Fan and Guoliang Fan Visual Computing and.

Modeling Pixel Process with Scale Invariant Local Patterns for Background Subtraction in Complex Scenes (CVPR’10) Shengcai Liao, Guoying Zhao, Vili Kellokumpu,

A Bayesian algorithm for tracking multiple moving objects in outdoor surveillance video Department of Electrical Engineering and Computer Science The University.

Detection and Removal of Rain from Videos Department of Computer Science Columbia University Kshitiz Garg and Shree K. Nayar IEEE CVPR Conference June.

Object Detection and Tracking Mike Knowles 11 th January 2005

ENTERFACE ’10 Amsterdam, July-August 2010 Hamdi Dibeklio ğ lu Ilkka Kosunen Marcos Ortega Albert Ali Salah Petr Zuzánek.

Cliff Rhyne and Jerry Fu June 5, 2007 Parallel Image Segmenter CSE 262 Spring 2007 Project Final Presentation.

Agenda The Subspace Clustering Problem Computer Vision Applications

1 Motion in 2D image sequences Definitely used in human vision Object detection and tracking Navigation and obstacle avoidance Analysis of actions or.

LOCUS Demo Stefan Zickler. Two “different” classes Class “Car Side Views” Class “Car Rears”

Jason Li Jeremy Fowers Ground Target Following for Unmanned Aerial Vehicles.

ICCV 2003UC Berkeley Computer Vision Group Recognizing Action at a Distance A.A. Efros, A.C. Berg, G. Mori, J. Malik UC Berkeley.

Recognizing Action at a Distance A.A. Efros, A.C. Berg, G. Mori, J. Malik UC Berkeley.

What’s Making That Sound ?

Flow Based Action Recognition Papers to discuss: The Representation and Recognition of Action Using Temporal Templates (Bobbick & Davis 2001) Recognizing.

Project title : Automated Detection of Sign Language Patterns Faculty: Sudeep Sarkar, Barbara Loeding, Students: Sunita Nayak, Alan Yang Department of.

Dynamic 3D Scene Analysis from a Moving Vehicle Young Ki Baik (CV Lab.) (Wed)

Mentor: Salman Khokhar Action Recognition in Crowds Week 7.

Vision-based human motion analysis: An overview Computer Vision and Image Understanding(2007)

ECE 172A SIMPLE OBJECT DETECTOR WITH INDICATOR WHEN A NEW OBJECT HAS BEEN ADDED TO OR MISSING IN A ROOM Presented by by Hugo Groening.

Students: Meera & Si Mentor: Afshin Dehghan WEEK 4: DEEP TRACKING.

 Present by 陳群元.  Introduction  Previous work  Predicting motion patterns  Spatio-temporal transition distribution  Discerning pedestrians  Experimental.

Active Frame Selection for Label Propagation in Videos Sudheendra Vijayanarasimhan and Kristen Grauman Department of Computer Science, University of Texas.

IEEE International Conference on Multimedia and Expo.

Progress Report, Weeks 2 & 3 REU 2012 Paul Finkel May 30 th, 2012.

Quiz Week 8 Topical. Topical Quiz (Section 2) What is the difference between Computer Vision and Computer Graphics What is the difference between Computer.

Motion Features for Action Recognition YeHao 3/11/2014.

Robotics Chapter 6 – Machine Vision Dr. Amit Goradia.

Instructor : Dr. K. R. Rao Presented by : Vigneshwaran Sivaravindiran

IEEE 2015 Conference on Computer Vision and Pattern Recognition Active Learning for Structured Probabilistic Models with Histogram Approximation Qing SunAnkit.

Bio-inspired Learning for a Colony of Microsystems Rama Chellappa UMD.

Video object segmentation and its salient motion detection using adaptive background generation Kim, T.K.; Im, J.H.; Paik, J.K.; Electronics Letters

Goal: Predicting the Where and What of actors and actions through Online Action Localization Figure 1.

A. M. R. R. Bandara & L. Ranathunga

Performance Evaluation of Feature Detection for Local Optical Flow Tracking Tobias Senst, Brigitte Unger, Ivo Keller and Thomas Sikora.

Counting by 10s, 100s, 1000s Five-digit

Particle Filtering for Geometric Active Contours

Combining CNN with RNN for scene labeling (segmentation)

Nonparametric Semantic Segmentation

Research paper written by

Textual Video Prediction

Counting In High Density Crowd Videos

Counting in High-Density Crowd Videos

Counting in High-Density Crowd Videos

Online Graph-Based Tracking

Counting in High-Density Crowd Videos

Single Image Rolling Shutter Distortion Correction

Iterative Crowd Counting

Optical flow and keypoint tracking

John Burnum working under Salman Khokhar

Mentor: Salman Khokhar

Additive Manufacturing: Denoising and Particle Tracking

Multi-UAV Detection and Tracking

Multi-UAV to UAV Tracking

Moving Target Detection Using Infrared Sensors

Report 4 Brandon Silva.

Week 3 Volodymyr Bobyr.

CVPR 2019 Poster Presented by Xu Gao 2019/07/04

Presentation transcript:

Counting in High-Density Crowd Videos Edgar Lopez Mentor: Dr. Haroon Idrees

Video Annotations Method Compute forward tracks. Compute backward tracks. Match forward and backward tracks. Distance Appearance (Intensity Histogram) Added the constraint that if distance is large keep both tracks and don’t match. Compute average tracks by using weighting system. t frame t+step frame

Video Annotations Manually Annotated Frame

Video Annotations Annotations computed by our method.

Video Annotations Manually Annotated Frame

Motion for Counting and Segmentation Saad Ali and Mubarak Shah, A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, June 19-21, 2007.

Crowd Flow Segmentation Segmentation using Saad Ali’s method. Input (Video) Output (1 Segmentation Mask)

Density Segmentation Segmentation using our method. Input (1 Frame) Output (1 Segmentation Mask)

Crowd Segmentation Challenges with this Video Low Resolution Saad Ali’s Method Our Method Challenges with this Video Low Resolution Very noisy Hard to compute segmentation using density map.

Counting in Videos Compute superpixels for each frame. Compute counts for each superpixel in all frames. Compute trajectories. Use trajectories to construct a Conditional Random Field (CRF) to smooth/improve counts between frames.