Visible to the public Abnormal Traffic Congestion Recognition Based on Video Analysis

TitleAbnormal Traffic Congestion Recognition Based on Video Analysis
Publication TypeConference Paper
Year of Publication2020
AuthorsLiu, X., Gao, W., Feng, D., Gao, X.
Conference Name2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
Date PublishedAug. 2020
ISBN Number978-1-7281-4272-2
Keywordsabnormal road traffic events, abnormal traffic congestion recognition, China, Computer vision, daily traffic management, Deep Learning, deep video, feature extraction, image motion analysis, Kalman filters, learning (artificial intelligence), Metrics, motion state, Multi object tracking and detection, multitarget tracking, neural nets, object detection, object tracking, pubcrawl, public security, resilience, Resiliency, road traffic, road traffic flow data, road vehicles, Roads, Scalability, Sudden congestion, target tracking, time 60.0 s, Traffic congestion, traffic congestion events, traffic congestion incidents, Traffic detection, traffic engineering computing, traffic police, Trajectory, urban traffic management, Vehicle detection, vehicle flow, video analysis, video monitoring, video signal processing, video surveillance, video surveillance equipment

The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.

Citation Keyliu_abnormal_2020