Vision-Based Accident Anticipation and Detection Using Deep Learning
Ayush Verma, Manju Khari
Abstract
Traffic accidents are one of the significant causes of injury, death, hospitalization and disability. The Road Traffic Injuries Report 2021 by the World Health Organization (WHO) reflects that globally every year, 1.20 million lives are lost as a consequence of road traffic accidents. In addition, between 20 and 40 million more commuters suffer nonlethal wounds, with many sustaining disabilities due to their injury. The rapid development of artificial intelligence and computer vision techniques are generating new opportunities for intelligent traffic and safety systems. In many countries, dashboard cameras (dashcam) are widely used in human operated and autonomous vehicles. A smart and efficient and intelligent system that can anticipate and detect accidents from the dashcam mounted video camera will improve preparedness, prevention and accident management. This paper presents a computer vision-based accident anticipation and detection method. The proposed approach employs a spatial feature based Recurrent Neural Network (RNN) along with Long Short-Term Memory (LSTM) cells to anticipate and detect accidents through dashcam video of vehicles. This method can accomplish accident anticipation about 1.7 seconds prior to its occurring with about 80% recall and 71% precision.