Modular Ensemble Tracking

Object Tracking is a very important domain in computer vision. It was recently approached using classification techniques and still more recently using boosting methods. Boosting is a general method of producing an accurate prediction rule by combining rough and moderately inaccurate ones. We introduce in this paper a modular object tracking algorithm based on one of these boosting methods: Adaboost. Tracking is performed on homogeneous feature spaces and the final classification decision is obtained by combining the decisions made on each of these spaces. A classifier update stage is also introduced, that allows the method both to handle time-varying objects in real-time (using fast computable features) and to handle partial occlusions. We compare the performance of our algorithm with Ensemble Tracking algorithm on several real video sequences.