HUMAN TRAFFIC DETECTION BASED ON LIGHTWEIGHT MOBILENET-SSD MODEL

Using deep neural network models to identify and detect pedestrian targets has very high value. In the real high-density pedestrian detection scene, due to the impact of hardware foundation and network performance consumption, it is often necessary to select a network with high processing speed and low hardware requirements, while taking into account the continuous characteristics of video surveillance. Therefore, this paper selected the lightweight MobileNet-SSD network to efficiently process human head targets and introduced the method of inter-frame difference to effectively track the elliptical feature targets of the human head. The related mathematical methods were combined to achieve a high-performance pedestrian flow detection solution that counted pedestrians across the line. After comparing with the current first-class detection models on different datasets, the proposed method showed excellent detection performance.