面向高速公路的车辆视频监控分析系统

doi:10.19665/j.issn1001-2400.2021.05.021

Abstract

Abstract:

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.

Key words: highway video surveillance, vehicle monitoring, object detection, object tracking, multi-scale feature fusion

CLC Number:

TP23

MAO Zhaoyong,WANG Yichen,WANG Xin,SHEN Junge. Vehicle video surveillance and analysis system for the expressway[J].Journal of Xidian University, 2021, 48(5): 178-189.

Figures/Tables 17

References 16

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真实情况	预测结果
真实情况	正例(Positive)	反例(Negative)
真(True)	TP(真正例)	FN(真反例)
假(False)	FP(假正例)	TN(假反例)

实验编号	检测模型	模型参数量大小/MB	mAP/%	平均耗时/ms
1	YOLOv3	246.4	83.37	17
2	YOLOv3-tiny	34.7	76.05	6
3	YOLOv3-EffiNet	62.9	91.47	11
4	YOLOv3-EffiNet+BiFPN	67.6	97.11	18

检测模型	YOLOv3	YOLOv3-tiny	YOLOv3-EffiNet	YOLOv3-EffiNet+BiFPN
小汽车	92.35	83.51	98.03	98.73
货车	86.08	78.24	99.84	98.09
出租车	78.13	72.62	79.34	97.75
面包货车	76.94	69.81	88.65	93.85

检测模型	上行车辆/辆	下行车辆/辆	总车辆数/辆	准确率/%	平均帧速度/(帧/秒)
YOLOv3+DeepSORT	692	521	1 213	81.9	31
YOLOv3-tiny+DeepSORT	427	339	766	51.7	142
YOLOv3-EffiNet+DeepSORT	718	557	1 275	86.1	76
YOLOv3-EffiNet+BiFPN+DeepSORT	792	590	1 382	93.3	31

异常事件种类	真实数量	错检数量	漏检数量	检测数量	准确率/%	平均帧速度 /(帧/秒)
异常停车	98段	0段	7段	91段	92.9	29
拥堵	117段	0段	4段	113段	96.6	29
逆行	57段	1段	4段	53段	91.2	28

Vehicle video surveillance and analysis system for the expressway

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