Adaptive multiple object tracking using colour and segmentation cues

Authors:
Pankaj Kumar;Michael J. Brooks;Anthony Dick
Affiliations:
University of Adelaide, School of Computer Science, South Australia;University of Adelaide, School of Computer Science, South Australia;University of Adelaide, School of Computer Science, South Australia
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

Citing 10
Cited 1

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Abstract

We consider the problem of reliably tracking multiple objects in video, such as people moving through a shopping mall or airport. In order to mitigate difficulties arising as a result of object occlusions, mergers and changes in appearance, we adopt an integrative approach in which multiple cues are exploited. Object tracking is formulated as a Bayesian parameter estimation problem. The object model used in computing the likelihood function is incrementally updated. Key to the approach is the use of a background subtraction process to deliver foreground segmentations. This enables the object colour model to be constructed using weights derived from a distance transform operating over foreground regions. Results from foreground segmentation are also used to gain improved localisation of the object within a particle filter framework. We demonstrate the effectiveness of the approach by tracking multiple objects through videos obtained from the CAVIAR dataset.