Special Traffic Event Detection: Framework, Dataset Generation, and Deep Neural Network Perspectives - Lee, Soomok; Lee, Sanghyun; Noh, Jongmin; Kim, Jinyoung; Jeong, Harim (Sensors, 2023)
Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm.
Keywords: Accident detections; Classification algorithm; Control management; Detection framework; Event recognition; Events detection; Road event recognition; Scene classification; Special traffic accident detection; Traffic event; road event recognition; scene classification; special traffic accident detection