面向高速公路的车辆视频监控分析系统

With the rapid development of video surveillance technology in the application of road safety, in order to realize the intelligent management of the expressway, this paper proposes a vehicle video surveillance and analysis system for the expressway. By detecting and tracking the vehicles for the surveillance videos, the applications of expressway related vehicle monitoring are further realized. The system presents a lightweight vehicle detection and tracking algorithm based on bidirectional pyramid multi-scale integration. The algorithm uses the lightweight network EfficientNet based on YOLOv3, and uses the bidirectional feature pyramid network (BiFPN) for multi-scale feature fusion. This system could ensure the real-time detection and improve the detection accuracy. Furthermore, in this paper, a multi-scene-highway-vehicles dataset is constructed by collecting freeway monitoring videos. Experimental results of this dataset shows that the detection accuracy of the proposed algorithm is 97.11%, which is 16.5% higher than that of the original YOLOv3 detection algorithm, and that the algorithm could run in real time at 31fps on vehicle tracking by combining with the DeepSORT model. At the same time, the vehicle monitoring system could realize multi-channel real-time detections in the field of vehicle flow statistics and traffic abnormal event detection, which is of practical application value.