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2007

Sequential Monte Carlo tracking by fusing multiple cues in video sequences

11 years 5 months ago
Sequential Monte Carlo tracking by fusing multiple cues in video sequences
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. Keywords – particle filtering, tracking in video sequences, colour, texture, edges, multiple cues, Bhattacharyya distance
Paul Brasnett, Lyudmila Mihaylova, David R. Bull,
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where IVC
Authors Paul Brasnett, Lyudmila Mihaylova, David R. Bull, Cedric Nishan Canagarajah
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