Feature Particles Tracking for Moving Objects

Abstract

For particle filtering tracking method, particle choosing was random to some degree according to the dynamics equation, which may cause inaccurate tracking results. To compensate, an improved particle filtering tracking method was presented. A moving vehicle was detected by redundant discrete wavelet transforms method (RDWT), and then the key points were obtained by scale invariant feature transform. The matching key points in the follow-up frames obtained by SIFT method were used as the initial particles to improve the tracking performance. Experimental results show that more particles centralize in the region of motion area by the presented method than traditional particle filtering, and tracking results of moving vehicles are more accurate; the run-time is only 0.15s more than traditional method, indicating that it has a certain actual application prospect.