基於圖形結構模型與匹配之人群異常事件偵測

ABNORMAL EVENT DETECTION OF HUMAN CROWDS BASED ON GRAPH MODELING AND MATCHING

黃柏菖(Po-Chung Huang)

指導教授 ： 陳敦裕

元智大學/電機通訊學院/電機工程學系/碩士(2011年)

摘要

人群分析在現今的視訊監控當中是一個重要的議題，由於人群的形狀為非固定狀態所以是一項艱鉅的挑戰。在本篇論文中，我們將要去對人群建立模型來描述人群運動的形變，首先透過Haar-like的方式對場景中人群的資訊做抽取。再者透過過濾人群資訊的雜訊後可以的到場景中獨立人(群)之所在。接著藉由Delaunay triangulation的方法有系統地將獨立的人群資訊串起來形成Graph結構。再透過線性代數方法以及外部輪廓和內部三角形的變異，來描述異常事件的發生機率，最後透過異常事件機率來決定場景中是否發生異常。實驗結果顯是我們的系統對於異常事件的偵測在非受控環境下是有效的。

關鍵字

圖形 ； 三角化 ； 人群分析 ； 異常事件偵測

並列摘要

Modeling human crowds is an important issue for video surveillance and is a challenging task due to their nature of non-rigid shapes. In this paper, for real time constraint, Haar-like features are first employed to approximately locate the position of an isolated region that comprise an individual person or a set of occluded persons. Each isolated region is considered a vertex and a human crowd is thus modeled by a graph. To regularly construct a graph, Delaunay triangulation is used to systematically connect vertices and therefore the problem of event detection of human crowds is formulated as measuring the topology variation of consecutive graphs in temporal order. To effectively model the topology variation, local characteristics such as triangle deformations and eigenvalue-based subgraph analysis, and global features such as moments are all computed and finally combined as an indicator to detect if any anomalies of human crowd(s) present in the scene. Experimental results obtained by using extensive dataset show that our system is effective in detecting anomalous events for uncontrolled environment of surveillance videos.

並列關鍵字

graph ； triangulation ； human crowd analysis ； abnormal event detection

參考文獻

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