Outlier Detection on Vehicle Trajectories for discovering Roam-Event Trajectories

• Main Authors: Shann, Shi-Han, 單師涵
• Other Authors: Liu, Duen-Ren
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/65516248185626372269

碩士 === 國立交通大學 === 資訊管理研究所 === 100 === While the implementation of Automatic Number Plate Recognition (ANPR) arises due to the massive use of surveillance camera and Closed-circuit television (CCTV) in public area, numerous implementation on policing including traffic violation, vehicle tracing are adopted. In addition, since policing had evolved from crime investigation to Intelligence-Led Policing (ILP), implementing ANPR as a decision support system for policing has become a possible trend. Observing crimes through vehicle use nowadays, though National Police Agency (NPA) announced that there are several known abnormal patterns including stagnation, grouping and roam in vehicle crimes, but the most prospect part, roam behavior, are yet to be investigated in data mining. Regarding that a premeditated crime event may contains more than one roaming trajectories, we first develop a convincible premise of roam behavior through the purpose of finding premeditated crime event-related roaming trajectories, we then propose a shape-based mechanism jointed with an event-relevant trajectory identification technique to detect possible crime-related roam behaviors. Finally, Top-k possible suspicious crime events are identified and presented to aid policing. Experiment comparisons show that the proposed method performs better than DBSCAN outlier detection method. Moreover, the proposed method is advanced in providing event-relevant information.