On-line trajectory clustering for anomalous events detection

被引：175

作者：

Piciarelli, C. ^{[1
]}

Foresti, G. L. ^{[1
]}

机构：

[1] Univ Udine, Dept Math & Comp Sci, I-33100 Udine, Italy

来源：

PATTERN RECOGNITION LETTERS | 2006年 / 27卷 / 15期

关键词：

trajectory clustering; on-line clustering; behaviour analysis;

D O I：

10.1016/j.patrec.2006.02.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a trajectory clustering algorithm suited for video surveillance systems. Trajectories are clustered on-line, as the data are collected, and clusters are organized in a tree-like structure that, augmented with probability information, can be used to perform behaviour analysis, since it allows the identification of anomalous events. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：1835 / 1842

页数：8

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