Sequential Monte Carlo tracking by fusing multiple cues in video sequences

Brasnett, P and Mihaylova, L (2007) Sequential Monte Carlo tracking by fusing multiple cues in video sequences. Image and Vision Computing, 25 (8). pp. 1217-1227. ISSN 0262-8856

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This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visual models is presented along with a method for adaptively weighting the cues when multiple models are used. A particle filter (PF) is designed for object tracking based on multiple cues with adaptive parameters. Its performance is investigated and evaluated with synthetic and natural sequences and compared with the mean-shift tracker. We show that tracking with multiple weighted cues provides more reliable performance than single cue tracking.

Item Type:
Journal Article
Journal or Publication Title:
Image and Vision Computing
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The final, definitive version of this article has been published in the Journal, Image and Vision Computing, 25 (8), 2007, © ELSEVIER.
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Deposited On:
10 Dec 2007 08:44
Last Modified:
22 Oct 2021 01:57