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Multiple People Tracking-by-Detection in a Multi-camera Environment

Automatic vision-based people tracking system promises to be the key for a large variety of domains. Although huge research efforts have been made to increase the performance, the robustness and accuracy of tracking systems is still far from being satisfactory. 
This thesis develops a complete system for long-term detection and tracking of multiple people in complex and crowded multi-camera environment, with a hierarchical grid-based, globally optimal tracking-by-detection strategy. 
We show that a finer analysis of human behavior can contribute to better resolving ambiguities between crossing trajectories. A novel evaluation framework is proposed, to quantitatively evaluate the performance of proposed system through experiments on a large variety of benchmark video sequences.