NDLI: Finding and tracking people from the bottom up

Content Provider	IEEE Xplore Digital Library
Author	Ramanan, D. Forsyth, D.A.
Copyright Year	2003
Description	Author affiliation: Comput. Sci. Div., Univ. of California, Berkeley, CA, USA (Ramanan, D.; Forsyth, D.A.)
Abstract	We describe a tracker that can track moving people in long sequences without manual initialization. Moving people are modeled with the assumption that, while configuration can vary quite substantially from frame to frame, appearance does not. This leads to an algorithm that firstly builds a model of the appearance of the body of each individual by clustering candidate body segments, and then uses this model to find all individuals in each frame. Unusually, the tracker does not rely on a model of human dynamics to identify possible instances of people; such models are unreliable, because human motion is fast and large accelerations are common. We show our tracking algorithm can be interpreted as a loopy inference procedure on an underlying Bayes net. Experiments on video of real scenes demonstrate that this tracker can (a) count distinct individuals; (b) identify and track them; (c) recover when it loses track, for example, if individuals are occluded or briefly leave the view; (d) identify the configuration of the body largely correctly; and (e) is not dependent on particular models of human motion.
Sponsorship	IEEE Comput. Soc Tech. Committee on Pattern Anal. & Machine Intelligence (TCPAMI)
File Size	815799
File Format	PDF
ISBN	0769519008
ISSN	10636919
DOI	10.1109/CVPR.2003.1211504
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-06-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Humans Predictive models Particle filters Clothing Computer science Clustering algorithms Acceleration Tracking loops Inference algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Simultaneous clustering and tracking unknown number of objects

Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference

Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis

Weighted Bayesian Network for Visual Tracking

Robust online appearance models for visual tracking

Action Spaces for Efficient Bayesian Tracking of Human Motion

Tracking 3D Human Body using Particle Filter in Moving Monocular Camera

Estimating contact dynamics

Full body tracking-based human action recognition

Finding and tracking people from the bottom up

Similar Documents

Simultaneous clustering and tracking unknown number of objects

Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference

Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis

Weighted Bayesian Network for Visual Tracking

Robust online appearance models for visual tracking

Action Spaces for Efficient Bayesian Tracking of Human Motion

Tracking 3D Human Body using Particle Filter in Moving Monocular Camera

Estimating contact dynamics

Full body tracking-based human action recognition

Finding and tracking people from the bottom up