Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/44767
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Type: Conference paper
Title: An adaptive Bayesian technique for tracking multiple objects
Author: Kumar, P.
Brooks, M.
Van Den Hengel, A.
Citation: Pattern Recognition and Machine Intelligence / David Hutchison ... [et al.] (eds.):657-665
Publisher: Springer
Publisher Place: Germany
Issue Date: 2007
Series/Report no.: Lecture Notes in Computer Science ; 4815/2007
ISBN: 3540770453
9783540770459
ISSN: 0302-9743
1611-3349
Conference Name: International Conference on Pattern Recognition and Machine Intelligence [PReMI] (2nd : 2007 : Kolkata, India)
Statement of
Responsibility: 
Pankaj Kumar, Michael J. Brooks and Anton van den Hengel
Abstract: Robust tracking of objects in video is a key challenge in computer vision with applications in automated surveillance, video indexing, human-computer-interaction, gesture recognition, traffic monitoring, etc. Many algorithms have been developed for tracking an object in controlled environments. However, they are susceptible to failure when the challenge is to track multiple objects that undergo appearance change to due to factors such as variation in illumination and object pose. In this paper we present a tracker based on Bayesian estimation, which is relatively robust to object appearance change, and can track multiple targets simultaneously in real time. The object model for computing the likelihood function is incrementally updated and uses background-foreground segmentation information to ameliorate the problem of drift associated with object model update schemes. We demonstrate the efficacy of the proposed method by tracking objects in image sequences from the CAVIAR dataset.
Description: The original publication can be found at www.springerlink.com
RMID: 0020075479
DOI: 10.1007/978-3-540-77046-6_81
Appears in Collections:Computer Science publications

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