Abstract: In complex environments,because of high density and movement randomness of pedestrians,it is too difficult to detect and track moving targets,therefore leading to counting errors．An algorithm of detecting and tracking moving targets combining the MB-LBP(Multi-scale Block Local Binary Pattern) feature extraction and particle filter algorithm was proposed in this paper．Firstly,we adopted the algorithm of AdaBoost to extract the MB-LBP features which are trained to generate a classifier,detect head targets,and remove part of false detection based on the size ranges of head targets．Secondly,we improved the original particle filter algorithm to predict and track moving targets．Finally,we counted the moving targets which are tracked．The experiments show that the algorithm can effectively detect and track multiple moving targets in complex environments,and count pedestrians in video frames accurately and rapidly.

Key words: MB-LBP,AdaBoost,Particle filter,Moving target detection,Moving target tracking