Abstract
Traffic incidents endanger the smooth running of vehicles. Congestion caused by traffic incidents has caused a waste of time and fuel and seriously affected transportation efficiency. At present, most methods use manual judgment or image features to detect traffic incidents, but these methods lack timeliness, leading to secondary incidents. For dangerous road sections such as ramp-free and long downhills, this paper proposes an algorithm to quickly detect traffic incidents based on a spatiotemporal map of vehicle trajectories. First, a vehicle dataset from the monitoring perspective is constructed, and an improved YOLOv4 detection algorithm is used to detect images organized as batches. Based on the detection result, the multi-object tracking method of vehicle speed prediction in key frames is used to obtain the vehicle trajectory. Then according to the vehicle trajectory obtained in a single scene, the vehicle trajectory is reidentified and associated in the continuous monitoring scene to construct a long-distance vehicle trajectory spatiotemporal map. Finally, according to the distribution and generation status of the trajectory in the spatiotemporal map, traffic incidents such as vehicle parking, vehicle speeding, and vehicle congestion are analyzed. Experimental results show that the proposed method greatly increases the speed of vehicle detection and tracking and obtains high mAP, MOTA, and MOTP indicators. The global spatiotemporal map constructed by trajectory reidentification can achieve high detection rates for traffic incidents, reduce the average elapsed time, and avoid the problems of the inaccuracy of analyzing image features.
Highlights
Real-time detection of traffic incidents can reduce congestion caused by traffic incidents
A national survey [2] found that because Automatic Incident Detection (AID) has the characteristics of complexity, a high false alarm rate, and delay, 90% of the survey respondents believe that such methods are not suitable
In the vehicle detection method, we stitch the images into batches for object detection and return the batch detection results to each image
Summary
Real-time detection of traffic incidents can reduce congestion caused by traffic incidents. Traffic incident detection includes abnormal parking, traffic congestion, and retrograde vehicles. Every 7 minutes of delay in a traffic incident increases the traffic jam by 1 mile [1], which increases the possibility of secondary incidents. Managers must detect and deal with the incident in time. Pattern recognition and genetic algorithms have been used to quickly detect traffic incidents. A national survey [2] found that because Automatic Incident Detection (AID) has the characteristics of complexity, a high false alarm rate, and delay, 90% of the survey respondents believe that such methods are not suitable
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have