부산대학교 도서관

For a long time, traffic surveillance cameras have been of great help in dealing with traffic incidents, monitoring, and analyzing driver behavior, and urban traffic planning. However, traffic monitoring video data are vast and so often have a high computational cost in post analysis and statistics, and the responsiveness is also an issue. In order to more comprehensively obtain vehicle behavior information at intersections, more and more surveillance cameras are being replaced by fisheye cameras, which makes traditional vehicle identification and tracking more difficult. Therefore, this study proposes a system architecture designed for the management of fisheye video data at intersections in scenarios of police traffic monitoring. Automatically identifying and tracking vehicles and processing their trajectories into subsequent semi-structured data that can be easily searched and counted, increases the possibilities of data usage.