Branch and bound global optima search for tracking a single object in a network of non-overlapping cameras

The paper presents a novel approach for single object tracking across non-overlapping camera views, which searches for the optimal association of single view trajectories. We map the tracking problem to a tree structure and introduce a branch and bound approach to efficiently explore the search space. We use an optimization criterion based solely on the geometric cues coming from the calibration of the network. The cost function is defined as to enforce consistency of geometrical and kinematic properties over the whole trajectory path. We show how the information content of the geometric properties of the network brings a substantial contribution to solve the association problem. Experiments in a set-up of four cameras using both synthetic and real trajectories validate the advantages of the approach both in terms of performance and information content of the geometric cue.