Moving vehicle tracking based on SIFT active particle choosing

Abstract

For particle filtering tracking method, particle choosing is random to some degree according to the dynamics equation, which may cause inaccurate tracking results. To compensate, an improved particle filtering tracking method is presented. A moving vehicle is detected by redundant discrete wavelet transforms method (RDWT), and then the key points are obtained by scale invariant feature transform. The matching key points in the follow-up frames obtained by SIFT method are 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 method has been adopted by Tianjin traffic bureau of China, and has a certain actual application prospect. © 2009 Springer Berlin Heidelberg.