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Learning Spatio-Temporal Topology of a Multi-Camera Network by Tracking Multiple People

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Executive Summary

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). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, the authors 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, they 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.

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