计算机工程与应用2016，Vol.52Issue(14)：7-11,5.DOI:10.3778/j.issn.1002-8331.1601-0050
基于轨迹聚类的公共安全异常检测
Anomaly detection of public safety based on trajectory clustering
摘要
Abstract
The demand of public security anomaly detection is becoming more urgent. The approaches based on trajectories clustering become more popular in surveillance, but the existing methods are not good at high-dimensional and unequal-length trajectories. So this paper presents a new approach to cluster trajectories by combining the dynamic time warping and density-peak algorithm. It measures the distance between trajectories by dynamic time warping, and then clusters trajectories by density-peak cluster algorithm. Dynamic time warping can be directly used to measure the distance of trajectories through non-uniform sampling. Density-peak algorithm is a recently proposed cluster algorithm for non-spherical distribution data by combining the local density and the nearest distance. Experiments are conducted on PETS 2006 surveillance video datasets, and results prove that the proposed approach has an effective ability to discover anomaly patterns.关键词
轨迹聚类/异常检测/密度峰算法/公共安全Key words
trajectory clustering/anomaly detection/density peak algorithm/public safety分类
信息技术与安全科学引用本文复制引用
康凯,王家宝,刘方鑫..基于轨迹聚类的公共安全异常检测[J].计算机工程与应用,2016,52(14):7-11,5.基金项目
江苏省自然科学基金（No.BK20140065）；江苏省工程技术研究中心（No.BM2014391）；国家自然科学基金（No.61174198）；国家自然科学基金联合基金（No.U1435218）。 （）
