Research on Automatic Detection and Spatiotemporal Positioning Algorithm for Traffic Incidents Based on Video Sequence Analysis

This paper proposes an advanced methodology for the automatic detection and spatiotemporal positioning of traffic incidents using video sequence analysis. Traditional traffic monitoring methods often suffer from limitations such as poor accuracy and lack of real time adaptability. To address these challenges, the paper introduces a dynamic and integrated system that combines a dynamic traffic incident detection model (DTIDM) and a spatiotemporal positioning strategy (SPS). The DTIDM processes video sequences to extract relevant features and employs machine learning algorithms for accurate incident detection, while the SPS uses spatiotemporal data to accurately localize the detected incidents. The DTIDM is designed to handle large scale video data and adapt to diverse traffic conditions. It utilizes deep learning techniques for both spatial and temporal feature extraction, ensuring that the system can process real time video inputs efficiently. Furthermore, the SPS incorporates advanced data fusion techniques to integrate spatial and temporal data, which helps in pinpointing the exact location and time of incidents. This strategy ensures precise positioning and enables effective traffic management and response. Experimental results validate the system’s effectiveness, showing significant improvements in detection accuracy and incident localization compared to existing methods. The combination of these components provides a comprehensive solution to enhance traffic safety and management. The paper also identifies future areas for improvement, particularly in optimizing computational efficiency and integrating additional data sources to handle challenging real world scenarios like adverse weather conditions.