NDLI: Classification-Based Likelihood Functions for Bayesian Tracking

Content Provider	IEEE Xplore Digital Library
Author	Chunhua Shen Hongdong Li Brooks, M.J.
Copyright Year	2006
Description	Author affiliation: National ICT Australia, Australia; Australian National University, Australia (Chunhua Shen; Hongdong Li) \|\| University of Adelaide, Australia (Brooks, M.J.)
Abstract	The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well and those that do not. This paper describes a general framework for learning probabilistic models of objects for exploiting these models for tracking objects in image sequences. We use a discriminative classifier to learn models of how they appear in images. In particular, we use a support vector machine (SVM) for training, which is able to extract useful non-linear information, and thus represent more complex characteristics of the tracked object and background. This is a particular advantage when tracking deformable objects and where appearance changes due to the unstable illumination and pose occur. A by-product of the SVM training procedure is the classification function, with which the tracking problem is cast into a binary classification problem. An object detector directly using the classification function is then available. To make the tracker robust, an object detector that directly uses the classification function is combined into the tracker for object verification. This provides the capability for automatic initialisation and recovery from momentary tracking failures. We demonstrate improved robustness in image sequences.
Starting Page	33
Ending Page	33
File Size	306206
Page Count	1
File Format	PDF
ISBN	0769526888
DOI	10.1109/AVSS.2006.33
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-22
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Filtering Bayesian methods Support vector machine classification Object detection Detectors Robustness Image sequences Data mining Particle tracking
Content Type	Text
Resource Type	Article

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
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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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