Head Count People in Python

This paper proposes a novel people counting method based on head detection and tracking to evaluate the number of people who move under an over-head camera. There are four main parts in the proposed method: foreground extraction, head detection, head tracking, and crossing-line judgment. The proposed method first utilizes an effective foreground extraction method to obtain foreground regions of moving people, and some morphological operations are employed to optimize the foreground regions. Then it exploits a LBP feature based Adaboost classifier for head detection in the optimized foreground regions. After head detection is performed, the candidate head object is tracked by a local head tracking method based on Meanshift algorithm. Based on head tracking, the method finally uses crossing-line judgment to determine whether the candidate head object will be counted or not. Experiments show that our method can obtain promising people counting accuracy about 96% and acceptable computation speed under different circumstances.