Feature Points Based Multiple Moving Targets Tracking
基于特征点的多运动目标跟踪

For the widely demanding of adaptive multiple moving targets tracking, a type of feature based multi-target tracking algorithm is presented. Background is adaptively modeled by Marr wavelet kernel function and a background subtraction technique based on redundant discrete wavelet transforms is used to detect multiple moving targets. A type of particle filtering combined with SIFT key points is used for tracking, and a queue chain method is used to record data association among different targets, which can improve the detection accuracy and reduce the complexity. Actual tests show that the algorithm can track multi-target with a better performance of real time and mutual occlusion robustness; it can be used in intelligent monitoring with extensive application prospect.