Tracking of multiple objects across multiple cameras with overlapping and non-overlapping views

In this paper, 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. For single camera cases, Kalman filter and adaptive particle sampling are integrated for multiple objects tracking. When extended to multiple cameras cases, 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.