Trajectory pattern learning approach based on the normalized edit distance and spectral clustering algorithm

For the inaccuracy problem of using Euclidean and Hausdorff distances to measure the trajectories' difference, a motion trajectory learning approach is developed based on the normalized edit distance and spectral clustering algorithm. Firstly, the trajectories are recoded through vector quantization. Then, a normalized edit distance is adopted to measure the difference among the trajectories. After that, the spectral clustering algorithm is applied to obtain the trajectories' distribution patterns based on the pair-wise distance matrix. Finally the learned patterns are used to detect the local and global anomaly. Experiments on synthetic and real world data sets demonstrate the effectiveness of our proposed approach to trajectory analysis and anomaly detection.