IR target tracking based on mean shift and particle filter

A novel method for infrared target tracking which combines mean shift and particle filter was proposed in order to improve the tracking accuracy and robustness. Based on the particle filter, the mean shift was introduced as an iterative mode seeking procedure, in which particles move toward the maximal posterior kernel density estimation of target state. The weights of particle samples are updated as the mean shift iterative operating. The posterior distribution of the infrared target is approximated by a set of re-weighted samples, while the infrared target tracking is implemented by the particle filter algorithm which constructed by the sample set. Experimental results show that the proposed method is more effective and robust than the independent standard particle filter and mean shift.