Anomalies Detection in Traffic Video Stream Using Computer Vision and Deep Learning
DOI:
https://doi.org/10.31649/1997-9266-2025-182-5-146-155Keywords:
anomaly detection, traffic surveillance, YOLOv8, OC-SORT, homography, computer visionAbstract
The article considers the problem of detecting anomalies in the video stream from traffic surveillance cameras, which is an important element of modern intelligent transport systems. An object-centric approach “detector-tracker-analyzer of anomalies” is proposed, which provides increased reliability of automatic detection of anomalous events, in particular, driving at a prohibitory traffic light signal, speeding, emergency braking and prolonged stay in the intersection zone. The method is based on the modern object detector YOLOv8n, which is characterized by high processing speed and acceptable accuracy for real-time tasks, as well as the OC-SORT tracker, which demonstrates increased resistance to short-term occlusions and reduces the number of false associations in a dense traffic flow. To determine the speed characteristics of vehicles, a homographic transformation was used taking into account the camera calibration, which made it possible to correctly translate the coordinates of objects into the world system.
To test the efficiency of the approach, the proprietary dataset based on open source videos (Friant Roulette channel, California) was generated, containing both normal and abnormal situations. A total of 160 video fragments were used. The experiments demonstrated that the use of the combination of YOLOv8n + OC-SORT provides higher accuracy compared to the basic combination of YOLOv8n + SORT, which is confirmed by the increase in the Matthews correlation coefficient (MCC) values. The best results were obtained for anomalies such as “running a red light” (MCC = 0.909) and “being in the intersection zone” (MCC = 0.881). The results for “speeding” (MCC = 0.815) and “emergency braking” (MCC = 0.505) were somewhat worse, which is explained by the sensitivity to the quality of the video stream, viewing angle and camera stability.
The developed approach is promising for the integration into intelligent traffic monitoring systems, as it allows automating the process of detecting violations and reducing the workload on operators. Further development directions include the optimization of the detector model architecture, using re-identification modules to reduce the number of false associations, improving speed estimation algorithms, and expanding the list of anomalies, such as pedestrians on the roadway or prohibited parking.
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