Tracking of Multiple Objects over Camera Networks with Overlapping and Non-overlapping Views

In this book chapter, we propose a fully automated approach for tracking of multiple objects across multiple cameras with overlapping and non-overlapping views in a unified framework without initial training or prior camera calibration. For tracking with a single camera, Kalman filter and adaptive particle-sampling techniques are integrated for multiple objects tracking. When extended to tracking over multiple cameras, the relations between adjacent cameras are learned systematically by using image registration techniques for consistent handoff of tracking-object labels across cameras. In addition, object appearance measurement is employed to validate the labeling results. Experimental results demonstrate the performance of our approach on real video sequences for cameras with overlapping and non-overlapping views.

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