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.
Adaptive Multiple Object Tracking Using Colour and Segmentation Cues
Computer Vision – ACCV 2007 ; 19 ; 853-863
Lecture Notes in Computer Science ; 4843 , 19
2007-01-01
11 pages
Article/Chapter (Book)
Electronic Resource
English
Object Model , Object Tracking , Visual Tracking , Foreground Pixel , Hypothesis Evaluation Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Biometrics , Algorithm Analysis and Problem Complexity
Segmentation Using Multiscale Cues
British Library Conference Proceedings | 2004
|Segmentation using multiscale cues
IEEE | 2004
|