Real-time people detection based on top-view TOF camera

This paper proposes a real-time people detection system based on time-of-flight (TOF) depth cameras, which monitors the flow of people in public places, such as subway entrances and exits and shopping mall passages. The proposed system mainly includes preprocessing, contour recognition, Neural Networks recognition, tracking and counting. It makes full use of the top-view depth information, avoids the problem of strabismus, and reduces the amount of calculation. At the same time, compared with the contour template matching algorithm, the accuracy is improved. This algorithm can improve the calculation speed while ensuring accuracy and robustness. Experiments show that the proposed system can run on the CPU platform at a speed of 20ms per frame. It also achieves high-precision head detection and counting, and the accuracy rates of single person and double person can reach 100% and the accuracy rates of the multi-person can reach 97%.