NDLI: Learning affinities and dependencies for multi-target tracking using a CRF model

Content Provider	IEEE Xplore Digital Library
Author	Bo Yang Chang Huang Nevatia, R.
Copyright Year	2011
Description	Author affiliation: University of Southern California, Institute for Robotics and Intelligent Systems, Los Angeles, CA 90089, USA (Bo Yang; Chang Huang; Nevatia, R.)
Abstract	We propose a learning-based Conditional Random Field (CRF) model for tracking multiple targets by progressively associating detection responses into long tracks. Tracking task is transformed into a data association problem, and most previous approaches developed heuristical parametric models or learning approaches for evaluating independent affinities between track fragments (tracklets). We argue that the independent assumption is not valid in many cases, and adopt a CRF model to consider both tracklet affinities and dependencies among them, which are represented by unary term costs and pairwise term costs respectively. Unlike previous methods, we learn the best global associations instead of the best local affinities between tracklets, and transform the task of finding the best association into an energy minimization problem. A RankBoost algorithm is proposed to select effective features for estimation of term costs in the CRF model, so that better associations have lower costs. Our approach is evaluated on challenging pedestrian data sets, and are compared with state-of-art methods. Experiments show effectiveness of our algorithm as well as improvement in tracking performance.
Starting Page	1233
Ending Page	1240
File Size	1758622
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995587
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Training Estimation Head Feature extraction Minimization
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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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
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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