부산대학교 도서관

As the number of vehicles owned by individuals increases, so does the occurrence of hit-and-run incidents. However, the investigation process is time-consuming and inefficient due to the need for manual review of long-duration CCTV footage to identify suspects. Therefore, it is essential to establish an automated system for identifying hit-and-run suspects to reduce unnecessary manpower consumption. This paper proposes a system that utilizes artificial intelligence technology on CCTV footage to automatically detect the moment of a 'hit-and-run' incident and track the offending vehicle. Upon receiving CCTV footage, the system undergoes processes of selecting the victim vehicle, estimating the time of the accident, and estimating the offending vehicle, thereby detecting the most probable hit-and-run offending vehicle. To address the vulnerabilities of existing vehicle accident detection methods and to reduce errors caused by irrelevant surrounding objects, the process of object segmentation and detection models, as well as object tracking and depth estimation models, were introduced to separately classify surrounding objects. Additionally, to enhance the detection performance of domestic vehicles captured in CCTV footage, a separate dataset was compiled to improve object detection performance. When this system was applied to actual hit-and-run cases recorded by CCTV, it accurately detected the time of the accident and demonstrated a processing speed of over 27fps in HD resolution video.