【摘要】 为了对视频监控设备采集到的轨迹数据进行聚类和异常检测,提出了一种新的轨迹摘要算法。使用了Jensen-Shannon Divergence(JSD)度量方法实现了轨迹数据的重采样,使得计算轨迹间相似度的准确性有所提高,并为后续滤波过程提供了必要的等采样点个数的轨迹数据;自适应地确定轨迹相似性的阈值,并采用非局部的思想,将轨迹数据进行冗余分组,同时识别出异常轨迹数据;从信号处理的角度对分组后的轨迹数据进行硬阈值滤波,经过合并得到摘要轨迹;此外,不受轨迹输入顺序的影响,并且提供了可视化的多尺度轨迹摘要结果。与具有噪声的基于密度的聚类(DBSCAN)算法的异常检测效果进行对比,所提算法在准确率(Precision)、召回率(Recall)以及F1指标上均有所提升。更多还原

【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.更多还原