  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.
