A Multi-Stream Approach to Mixed-Traffic Accident Recognition Using Deep Learning - Swee Tee Fu; Lau Bee Theng; Brian Loh Chung Shiong; Chris McCarthy; Mark Tee Kit Tsun (IEEE Access)
Road traffic accidents are a leading cause of injuries and fatalities globally, prompting extensive research into deep learning-based accident recognition models for their superior performance in computer vision tasks. However, most studies focus on non-mixed traffic environments, where detection is simpler due to predictable traffic patterns and uniform vehicle types. In contrast, mixed-traffic scenarios present greater challenges as diverse vehicles, motorcyclists, and pedestrians move unpredictably. Models relying on a single type of perception are effective in structured traffic but struggle to handle the complexities of mixed-traffic environments. This study proposes a novel multi-stream deep learning model called Accident Recognition in Mixed-Traffic Scene (ARMS), which integrates three distinct streams: the first stream analyzes the overall accident scene, the second focuses on mixed-traffic accident features, and the third examines vehicle motion abnormalities through object detection and tracking, aimed at improving road accident recognition accuracy in mixed-traffic environments at intersections. This model is trained and evaluated using datasets from CADP, UA-DETRAC, and supplementary online sources. The results demonstrate that the ARMS model achieves an accuracy of 93.3%, with performance improving significantly through the fusion of the individual streams. Additionally, the ARMS model was evaluated using two publicly available standard datasets, which further highlights its improved performance in recognizing mixed-traffic accidents compared to existing studies.