带重要点约束的经典轨迹相似度量新算法

A New Similarity Measurement Method for Classical Trajectories with Important Point Constraints

在经典轨迹的相似度量问题中提出一个新问题:假设有待相似度量的经典轨迹和实时轨迹,在经典轨迹中存在部分点为重要点,实时轨迹受这些重要点约束。针对此问题,将经典轨迹和实时轨迹作为相似度量的两序列,重要点作为实时轨迹的约束序列,利用带约束的最长公共子序列理论提出带重要点约束的经典轨迹相似度量基础算法和快速算法。实验结果表明,快速算法比基础算法的计算时间减少了36%以上。

A new problem is proposed in the similarity measurement of classical trajectories:assuming the classical trajectory and the real-time trajectory to be measured,some points in the classical trajectory are important points,and the real-time trajectory is constrained by these important points.To solve this problem,the classical trajectory and the real-time trajectory are regarded as two sequences of similarity measurement,and the important points are regarded as the constrained sequence of the real-time trajectory.Using the constrained longest common subsequence theory,a basic algorithm and a fast algorithm of classical trajectory similarity measurement with important point constraints are proposed.Experiments show that the calculation time of the fast algorithm is reduced by more than 36%compared with that of the basic algorithm.