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 data sets,the proposed method showed excellent detection performance.
国家科技期刊平台
NSTL
万方数据
维普
