A Novel Approach for Car Accident Detection and Analysis Using AIML Techniques

Abstract

Abstract: In the context of escalating concerns regarding road safety, this study introduces an automated traffic accident detection and analysis framework that utilizes surveillance footage. The approach synergistically combines the Motion Interaction Field (MIF) model and YOLO v5 algorithm to achieve accurate crash detection and precise vehicle classification. The primary objective is to develop an autonomous system for identifying and evaluating traffic events captured by security cameras. The MIF model extracts valuable insights from interactions among moving objects within images and videos, tailored explicitly for detecting damaged vehicles. Localization of damaged vehicles is achieved using the YOLO v5 algorithm, complemented by perspective transformation to enable vertical image representation. The unbiased finite impulse response (UFIR) technique, notable for its insensitivity to statistical noise data, effectively determines vehicle motion. Furthermore, this work delves into an in-depth analysis of car crashes, estimating impact speed and point of collision from an aerial perspective. This work encompasses meticulous accident clip analysis, precise car direction determination, and error identification. The outcomes underscore the potential of the proposed framework to revolutionize accident detection and analysis, contributing to heightened road safety and streamlined traffic management strategies.