Adaptive evolutional strategy of particle filter for real time object tracking

In this paper, we propose an efficient real time tracker that uses a differential evolution strategy within the particle filter framework. Particles are strategically propagated based on the maximum a posterior (most likely) object location with genetic operators. This enables the use of a small sample size and alleviates the frequent sample degeneracy and impoverishment problems encountered in particle filters. We reduce the sample size considerable while improving the trackers accuracy. This makes the proposed tracker a good candidate for real time object tracking or an embedded resource constrained tracker.