Abstract: People counting in the field of intelligent monitoring is important,but because of complex background environment and pedestrian movement occlusion phenomenon resulting in the current method accuracy is low,in addition to the traditional line statistics on the number of practical limited scope,taking into account the present that lacking effective methods,we proposed a method of people counting based on hot area.Firstly,the adaptive learning-rate background model is used to extract the foreground of the moving target,and the position and size of the foreground region are obtained.The HOG feature in the foreground region of the moving target is scanned,and the head and shoulder target is determined.Then the target matching Matrix algorithm based on KCF is used to track the head-shoulder target.Finally,the number of pedestrians is calculated by combining the target trajectory and people counting method based on hot area.The number of the video is 960×720 pixels with a resolution of 960×720 pixels.The correctness of the algorithm reached 93.1%,and it can meet the real-time requirement.The method we proposed combines the detection efficiency and accuracy,and has good effect in scenes with complex background environment,which can meet various practical application scenarios of people counting.

Key words: HOG,Adaptive learning-rate background model,Target transformation matrix,Hot area,KCF,People counting