pattern-based moving object tracking
Monitoring the locations of a large number of objects that travel in a certain space is a popular problem for its importance in various application scenarios. It brings us a challenge of how to efficiently handle large volumes of location updates required to guarantee the error bound among an object's current, actual location and its current location in the tracking system. Current solutions predict the future locations based on the recent movements of the moving object. However, it is reliable to predict the position in near future only and the prediction accuracy is poor in the long term. This paper is aimed at the above weakness by introducing the movement pattern in Euclidean space based on the historical trajectories of moving objects. Dominant path pattern is proposed and employed in the moving object tracking system, which can estimate where an object will go next and how to get there. Specifically, dominant path pattern is discovered and indexed by a novel access method of efficient query processing. In addition, the pattern mining techniques with consideration of the accuracy and coverage in dominant path patterns discovering are presented. The experiments demonstrate the superiority of the proposed method comparing to existing methods by up to 73\% (91\%) less overall location updates on practical Taxi(Truck) dataset.
moving object tracking trajectory dominant path pattern