Abstract In actual surveillance conditions, many uncertainties exist in pedestrian movement. These movements may disturb the current tracking algorithms and result in tracking lost. An adaptive pedestrian tracking algorithm is proposed. In this algorithm, the prior knowledge of pedestrian detection is embedded into the self-learning process of object model. Firstly, offline training is performed to get a set of sub-classifiers with strong discriminability and prior knowledge of the pedestrians. Then, online boosting algorithm is used for learning and updating the pedestrian's dynamic model from the offline trained sub classifier set. Experimental results show that the proposed method efficiently relieves the conflict between adaptation and drifting, and tracks pedestrian with various uncertain movement under the actual surveillance conditions.
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2009, Vol. 22 
