Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/29516
Type: Conference paper
Title: Probabilistic multiple cue intergration for particle filter based tracking
Author: Shen, C.
Van Den Hengel, A.
Dick, A.
Citation: Digital image computing : techniques and applications ; proceedings of the VIIth Biennial Australian Pattern Recognition Society Conference, DICTA 2003 / Sun C., Talbot H., Ourselin S. and Adriaansen T. (eds.), pp. 399-408
Publisher: CSIRO
Publisher Place: Australia
Issue Date: 2003
ISBN: 0643090398
Conference Name: Australian Pattern Recognition Society. Conference (7th : 2003 : Sydney, N.S.W.)
Statement of
Responsibility: 
Chunhua Shen, Anton van den Hengel, and Anthony Dick
Abstract: Robust visual tracking has become an important topic in the field of computer vision. The integration of cues such as color, edge strength and motion has proved to be a promising approach to robust visual tracking in situations where no single cue is suitable. In this paper, an algorithm is presented which integrates multiple cues in a probabilistic manner. Specifically the likelihood of each cue is calculated and weighted before Bayes’ rule is applied to obtain the resultant posterior. This posterior is generally not well represented analytically, and is therefore represented as a set of weighted particles, which is updated at each frame by a particle filter. This paper demonstrates how the combination of multiple cue integration and particle filtering results in a robust tracking method. We also demonstrate how each cue’s weight can be adapted on-line during the tracking procedure.
RMID: 0020032010
Published version: http://www.cmis.csiro.au/Hugues.Talbot/dicta2003/cdrom/pdf/0399.pdf
Appears in Collections:Computer Science publications

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