Adaptive multiple object tracking using colour and segmentation cues

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.