A particle filter tracking algorithm based on SIFT feature matching

This paper presents a particle filter tracking algorithm based on SIFT feature matching. After target prediction by particle filtering, SIFT features are calculated in the target neighborhood, so some unnecessary calculations are properly omitted, therefore the computational time is reduced greatly and the tracking speed is increased remarkably. Moreover, the features are calculated in the neighborhood of target, so its tracking robustness is increased correspondingly. Experimental results show that the proposed tracking algorithm can better adapt to target tracking under various conditions, with good performances of real-time and robustness.