Abstract:

In the early warning and surveillance system， the data mining technology can be used to mine the regular behavior from the massive target trajectory data， and realize intelligent awareness of situation information. At present， most regular behaviors mining methods only consider the spatial position information of target trajectory， ignoring the course and speed information of the target. It is thus difficult to distinguish the behaviors with similar spatial positions but different speeds and directions. In addition， trajectory clustering algorithms generally have complex parameter settings， and are easy to be affected by the distribution density of trajectory behavior. To solve the above problems， the spatiotemporal Hausdorff distance is firstly defined by constructing a time sliding window， which could measure the difference of multidimensional features of spatiotemporal trajectory. Secondly， based on the idea of k-nearest neighbor and the decision graph in density peak clustering， a regular behaviors mining algorithm is proposed based on fusion of multidimensional features of spatiotemporal trajectory. Finally， the simulated aircraft trajectory data and measured radar trajectory data are used for experimental analysis and verification. The results show that the proposed algorithm can accurately mine all behavior laws of targets in typical application scenarios， and has possible applications in intelligent surveillance tasks.

Key words: regular behaviors mining, spatiotemporal trajectory, multidimensional features, Hausdorff distance, trajectory clustering