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Journal of Electrical and Computer Engineering
Volume 2014, Article ID 645145, 11 pages
http://dx.doi.org/10.1155/2014/645145
Research Article

Activity-Based Scene Decomposition for Topology Inference of Video Surveillance Network

1Shanghai Advanced Research Institute, Chinese Academy of Sciences, China
2Shanghai Key Laboratory of Digital Media Processing and Transmission, China
3Shanghai Jiao Tong University, China

Received 14 October 2013; Accepted 12 January 2014; Published 26 February 2014

Academic Editor: Mohamad Sawan

Copyright © 2014 Hongguang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The topology inference is the study of spatial and temporal relationships among cameras within a video surveillance network. We propose a novel approach to understand activities based on the visual coverage of a video surveillance network. In our approach, an optimal camera placement scheme is firstly presented by using a binary integer programming algorithm in order to maximize the surveillance coverage. Then, each camera view is decomposed into regions based on the Histograms of Color Optical Flow (HCOF), according to the spatial-temporal distribution of activity patterns observed in a training set of video sequences. We conduct experiments by using hours of video sequences captured at an office building with seven camera views, all of which are sparse scenes with complex activities. The results of real scene experiment show that the features of histograms of color optic flow offer important contextual information for spatial and temporal topology inference of a camera network.