An Automatic Freeway Incident Detection Algorithm using Vehicle Trajectories

An automatic freeway incident detection method is important for maintaining a safe, efficient traffic operation. Due to the fact that a large number of surveillance videos may hinder the real-time and accurate response of current automatic incident detection algorithms, a comparative pessimistic likelihood estimation (CPLE) algorithm based on trajectory classification is proposed. A framework for automatic detection of anomalous events, which contains vehicle detection, vehicle tracking and trajectory classification, is developed. YOLO v3 is employed to detect the vehicles, and related information about four different types of vehicles is obtained. Online real-time tracking algorithms are used for multi-target tracking of vehicles. Anomalous event vehicle trajectories are obtained for different scenarios. Based on semi-supervised learning, the maximum likelihood method is employed to improve the classification of vehicle trajectories. CPLE is introduced and parameter setting and labeling are centered on comparison and pessimistic rules in order to classify and determine the incident trajectories, consequently, the automatic incident detection algorithm based on vehicle trajectories is proposed. The intelligent inspection system of Gansu Province G312 highway is used as a test object. A total of 1 300 videos were collected. Among them, 530 and 630 tracks are employed as test set and validation set, respectively. By testing difference scenarios of incidents and prewarning, the algorithm accuracy of trajectory classification based on CPLE reaches 89.7%, which is 23.6% higher than that of self-learning and 41.3% higher than that of supervised learning, respectively. Although the accuracy of scattered goods and speeding is averaged about 77.0%, the accuracy of sudden stopping, congestion, and accidents reaches 98.2%, and as for the incident detection influencing traffic seriously, the average accuracy reaches 94%. The proposed method enriches automatic incident detection algorithms and can be considered an alternative for freeway incident detection.