Traffic video based multiple moving targets tracking

For the demanding of adaptive multiple moving targets tracking in intelligent transportation field,a new type of traffic video widely based multi-target tracking algorithm was presented. Background was modeled by Marr wavelet probability kernel function and a background subtraction technique based on binary/redundant discrete wavelet transforms was introduced to detect multiple moving targets. After obtaining the foreground,shadow was eliminated by an edge detection method. A type of SI_P (SIFT-particle) filter combined with improved mean-shift method was used for video tracking,and tracking window adaptively changed its scale according to the size of target. A Queue chain method was used to record data association among different targets,which could improve the detection accuracy and reduce the complexity. The software is VC++6.0,and by actual road tests,the average runtime of SI_P algorithm is only 0.15 s more than that of traditional algorithm; the scale of tracking window can adaptively changes. The algorithm tracks multi-target with a better performance of real time and mutual occlusion robustness; it can be used in intelligent traffic monitoring with extensive application prospect.