Dynamic Color Tracking Based on Probabilistic Data Association

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  • Lu Xin
    Department of Information Science and Intelligent Systems, Faculty of Engineering, University of Tokushima
  • Oe Shunichiro
    Center for Advanced Information Technology, University of Tokushima

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This paper presents a color tracking method based on probabilistic data association in order to resolve difficult and complicated visual tracking problem, such as a changing of target’s representation, a clutter of environments and an interaction of target and camera. Because the probabilistic data association is flexible and suitable for ambiguous and missing data, which generates the difficulties of visual tracking, some methods of probabilistic data association could be combined and applied in this tracking method to find the solutions of these difficulties. Due to using sequential Monte Carlo framework, this tracking method is applied to tracking of changeful target by handling the related information between every frame in image sequences. In order to improve tracking accuracy, this method utilizes factorized sampling algorithm to express target characters as sample-set. Moreover, this method benefits from HSV color model and captures the color natures of object like human to enhance the color-sensing capability of computer. Hence, this method could be considered as self-learning system and imitate the based human vision function - tracking. The tracking system applying this method is implemented in real-time at around 15Hz with 640 × 480 pixels image. The results show that the self-learning and real-time system is able to track a target robustly with enough accuracy and automatically control the camera’s pan, tilt and zoom to remain the object centered in the field of vision.

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