Adaptive Object Tracking using Particle Swarm Optimization

This paper presents an automatic object detection and tracking algorithm by using particle swarm optimization (PSO) based method, which is a searching algorithm inspired by the behaviors of social insect in the nature. A cascade of boosted classifiers based on Haar-like features is trained and employed to detect objects. To improve the searching efficiency, first the object model is projected into a high-dimensional feature space, and the PSO-based algorithm is applied to search over this high-dimensional space and converge to some global optima, which are well-matched candidates in terms of object features. Then, a Bayes-based filter is used to identify the best match with the highest possibility among these candidates under the constraint of object motion estimation. The proposed algorithm considers not only the object features but also the object motion estimation to speed up the searching procedure. Experimental results of tracking on vehicle and face demonstrate that the proposed method is efficient and robust under dynamic environment.