Computer Vision-based Accident Detection in Traffic Surveillance

TL;DR

This paper introduces a novel computer vision framework utilizing Mask R-CNN and centroid tracking to detect road accidents in various weather and lighting conditions with high accuracy and low false alarms.

Contribution

The paper presents a new accident detection framework combining Mask R-CNN and centroid tracking, effective across diverse real-world conditions.

Findings

High detection rate achieved

Low false alarm rate demonstrated

Effective in various weather conditions

Abstract

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.