Vehicle Crash Prediction using Vision
Muhammad Haris, Minhaj Ahmed Moin, Farooq Abdul Rehman, Muhammad Farhan · 2021
Road accidents due to vehicles crashing each other not only result in loss of lives but also cause heavy damage to the infrastructure. Several infrastructural changes, for example, placement of mirror on blind corners, appropriate placement of traffic signs and lights etc., do reduce the number of vehicle crashes. However, there are numerous factors contributing to the crashes. It is understood that the last few seconds are crucial and if a crash can be predicted in them then it can be avoided. In this paper, we propose a computer vision based vehicle crash prediction pipeline which uses RetinaNet to detect vehicles, Kernalized Correlation filter to track them on road using CCTV view, and a Gaussian distribution approximation to predict the trajectories of vehicles in the video frames, by exploiting short term stationarity of the time process. The proposed prediction algorithm gives a short term prediction and yields quite precise results. Our proposed methodology is quite light as well as data independent as opposed to the ones in literature.