Kernel-based spatial-color modeling for fast moving object tracking

Babu, RV and Makur, Anamitra (2007) Kernel-based spatial-color modeling for fast moving object tracking. In: 32nd IEEE International Conference on Acoustics, Speech and Signal Processing, APR 15-20, 2007, Honolulu, HI.

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Abstract

Visual tracking has been a challenging problem in computer vision over the decades. The applications of Visual Tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy.

Item Type:	Conference Paper
Publisher:	IEEE
Additional Information:	Copyright 20o7 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	30 Mar 2010 12:06
Last Modified:	25 Feb 2019 05:49
URI:	http://eprints.iisc.ac.in/id/eprint/26281

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