Dynamic Knowledge-Based Tracking and Autonomous Anomaly Detection

This article presents a study on the problem of region surveillance in complex terrain using an unmanned aerial vehicle (UAV), and proposes a novel framework for on-road ground target tracking and detection of anomalous driving behavior with the assistance of domain-constrained information. In order to improve the accuracy of ground target tracking, terrain information is extracted and incorporated as constraints into the tracking process. To account for the dynamic changes in terrain-constrained information, a sliding window approach leveraging a dynamic programming algorithm is employed for domain-constrained knowledge inference. To improve the autonomy and intelligence of the monitoring UAV, a mechanism for recognizing suspicious driving patterns is seamlessly integrated into the target tracking process with the aid of domain knowledge. The effectiveness of the proposed method is validated using extensive numerical simulations.