关键词: 轨迹摘要, 重采样, 非局部去噪, 信号处理, 多尺度滤波

Abstract: In order to cluster and detect anomalies for the trajectory data collected by video surveillance equipment, a novel trajectory abstraction algorithm was proposed. Trajectories were firstly resampled by utilizing the Jensen-Shannon Divergence (JSD) measurement to improve the accuracy of similarity measurement between trajectories. Resampled trajectories in equal length, i.e. with the same number of sampling points, were required by the following non-local denoising. The similarity thresholds of the trajectory were determined adaptively, and the non-local means were used to cluster the trajectory data and identify the abnormal trajectory data. From the perspective of signal processing, the grouping trajectory data was filtered by the hard-thresholding method to get the summary trajector. The proposed algorithm was insensitive to the order of input trajectories and provides visual multi-scale abstractions of trajectory data. Compared with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, the proposed algorithm performs better in terms of precision, recall and F1-mearsure.

Key words: trajectory abstraction, resampling, non-local denoising, signal processing, multi-scale filtering