Computer Vision-based Accident Detection in Traffic Surveillance
This addresses the problem of real-time accident detection for traffic surveillance systems, but it is incremental as it builds on existing object detection and tracking methods.
The paper tackles the problem of detecting road accidents in traffic surveillance footage by proposing a framework that uses Mask R-CNN for object detection and centroid-based tracking to identify accidents based on speed and trajectory anomalies after vehicle overlaps. The result is a robust method achieving high detection rates and low false alarm rates across diverse conditions like daylight, low visibility, and adverse weather.
Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. In this paper, a neoteric framework for detection of road accidents is proposed. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time.