Trajectory Classification Based on Hausdorff Distance and Longest Common SubSequence

Considering the position and direction of trajectories of moving objects, a trajectory classification algorithm is proposed based on improved Hausdorff distance and Longest Common SubSequence (LCSS) to improve the trajectories classification. In this algorithm, the position similarity between trajectories is measured by the modified Hausdorff distances. And then the direction of the trajectories is distinguished by the modified LCSS distances. Comparing with other trajectory classification algorithms, the proposed algorithm compromises the merits of both Hausdorff distance and LCSS in trajectory classification and enhances the trajectory classification accuracy. Furthermore, to reduce the computational complexity of the similarity measure, a method of modified isometric transformation algorithm and an LCSS fast algorithm are realized. Experimental results show that the clustering accuracy of the proposed algorithm is greatly improved and the clustering accuracy rate can achieve 96%. Meanwhile, the computational cost is greatly reduced by the modified isometric transformation algorithm and the LCSS fast algorithm, and the magnitude of the declines can reach to 80%. The proposed algorithm can satisfy the system requirements of higher precision, real time and robustness.