Abstract:
Road accidents remain a major global concern, particularly at urban intersections where cars, motorcycles, and pedestrians interact in unpredictable and often chaotic ways. Traditional accident detection systems struggle in these mixed-traffic environments, especially in areas with minimal lane discipline and diverse road user behavior. To address this challenge, we introduce accident recognition in mixed-traffic scene (ARMS), an artificial intelligence (AI)-powered, multistream deep learning model that combines scene-level context, object-specific features, and motion abnormality tracking in a unified framework. Designed to handle the visual and behavioral complexities of real-world traffic, ARMS offers a promising step toward more accurate and timely accident detection in intelligent transportation systems.