Trajectory Association across Non-overlapping Moving Cameras in Planar Scenes

The ability to associate objects across multiple views allows co-operative use of an ensemble cameras for scene understanding. In this paper, we present a principled solution to object association where both the scene and the object motion are modeled. By making the motion model of each object with respect to time explicit, we are able to solve the trajectory association problem in a unified framework for overlapping or non-overlapping cameras. We recover the assignment of associations while simultaneously computing the maximum likelihood estimates of the inter-camera homographies and the trajectory parameters using the expectation maximization algorithm. Quantitative results on simulations are reported along with several results on real data.