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.
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Kumar, P., Brooks, M.J., Dick, A. (2007). Adaptive Multiple Object Tracking Using Colour and Segmentation Cues. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_81
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DOI: https://doi.org/10.1007/978-3-540-76386-4_81
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