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| Adaboost Object Tracking Algorithm |
| JIA Jing-Ping1, ZHANG Fei-Zhou1, CHAI Yan-Mei2 |
1.Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871
2.Department of Computer Science and Technology, Tsinghua University, Beijing 100084 |
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Abstract An Adaboost based algorithm for object tracking in image sequences is proposed. In this algorithm, tracking is considered as a binary classification problem. Firstly, the linear combination of R, G, and B with integer coefficients is used to generate the candidate features. Features are selected for the design of weak classifiers according to the two-class variance ratio. Then, a strong classifier is built on the weak classifiers. For each incoming frame, a likelihood image of the object is created according to the classification results of pixels by the strong classifier. The trust region method and the scale space theory are employed to locate the blobs in the likelihood image, and thus the object tracking is fulfilled. The changes of illumination often cause the changes of features. The adaptability of the proposed algorithm is improved by online integration of new weak classifiers and automated weights update of the used ones. Based on the tracking results of sequence examples, the proposed algorithm can adapt to feature changes, track object in cluttered background and describe the object accurately with better tracking precision.
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Received: 29 December 2007
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2009, Vol. 22 
