Robust object tracking with background-weighted local kernels

Jeyakar, Jaideep and Babu, RV and Ramakrishnan, KR (2008) Robust object tracking with background-weighted local kernels. In: Computer Vision and Image Understanding, 112 (3). pp. 296-309.

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Abstract

Object tracking is critical to visual surveillance, activity analysis and event/gesture recognition. The major issues to be addressed in visual tracking are illumination changes, occlusion, appearance and scale variations. In this paper, we propose a weighted fragment based approach that tackles partial occlusion. The weights are derived from the difference between the fragment and background colors. Further, a fast and yet stable model updation method is described. We also demonstrate how edge information can be merged into the mean shift framework without having to use a joint histogram. This is used for tracking objects of varying sizes. Ideas presented here are computationally simple enough to be executed in real-time and can be directly extended to a multiple object tracking system.

Item Type:	Journal Article
Publication:	Computer Vision and Image Understanding
Publisher:	Elsevier Science
Additional Information:	Copyright of this article belongs to Elsevier Science.
Keywords:	Mean shift;Object tracking;Kernel tracking.
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	30 Oct 2009 08:43
Last Modified:	25 Feb 2019 05:54
URI:	http://eprints.iisc.ac.in/id/eprint/17573

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