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Learning Spatio-Temporal Topology of a Multiple Cameras Network by Tracking Human Movement  

Nam, Yun-Young (아주대학교 유비쿼터스시스템연구센터)
Ryu, Jung-Hun (아주대학교 전자공학과)
Choi, Yoo-Joo (서울벤터정보대학원대학 컴퓨터공학과)
Cho, We-Duke (아주대학교 유비쿼터스시스템연구센터 전자공학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.13, no.7, 2007 , pp. 488-498 More about this Journal
Abstract
This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs) in Ubiquitous Smart Space (USS). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities.
Keywords
ubiquitous; smart space; surveillance; multiple cameras; people tracking; topology; inference;
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