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Particle Filtering with Multiple Cues for Object Tracking in Video Sequences

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date16/01/2005
Host publicationSPIE Proceedings
PublisherSPIE
Pages430-441
Number of pages12
Volume5685
Original languageEnglish

Conference

ConferenceIS &T/SPIE's 17th Annual Symposium on Electronic Imaging, Science and Technology
CitySan Jose California, USA
Period16/01/0520/01/05

Conference

ConferenceIS &T/SPIE's 17th Annual Symposium on Electronic Imaging, Science and Technology
CitySan Jose California, USA
Period16/01/0520/01/05

Abstract

In this paper we investigate object tracking in video sequences by using the potential of particle filtering to process features from video frames. A particle filter (PF) and a Gaussian sum particle filter (GSPF) are developed based upon multiple information cues, namely colour and texture, which are described with highly nonlinear models. The algorithms rely on likelihood factorisation as a product of the likelihoods of the cues. We demonstrate the advantages of tracking with multiple independent complementary cues compared to tracking with individual cues. The advantages are increased robustness and improved accuracy. The performance of the two filters is investigated and validated over both synthetic and natural video sequences.

Bibliographic note

Publisher: SPIE