Detection of Unusual Activities on the Road Using Deep CNN

Traffic accidents are a leading cause of violent deaths worldwide. The time delay in delivering medical responses to accident sites is heavily influenced by human factors, which directly affects the chances of survival. Given the widespread use of video surveillance systems and intelligent traffic systems, there is a growing need for automated traffic accident detection solutions. This paper presents an automated approach based on Deep Learning (DL) to detect traffic accidents from live video streams in real-time. The proposed method assumes that traffic accident events are described by visual features occurring through a temporal way. The 1 model architecture consists of a visual feature extraction phase, followed by temporal pattern identification, learned through convolution and recurrent layers using both built-from-scratch and public datasets. An accuracy comparable to state-of-the-art methods is achieved in the detection of accidents across various traffic scenarios, demonstrating the model's robust capability in accident recognition independent of road structure.