  Many state-of-the-art approaches to people tracking rely on detecting them in
each frame independently, grouping detections into short but reliable
trajectory segments, and then further grouping them into full trajectories.
This grouping typically relies on imposing local smoothness constraints but
almost never on enforcing more global constraints on the trajectories. In this
paper, we propose an approach to imposing global consistency by first inferring
behavioral patterns from the ground truth and then using them to guide the
tracking algorithm. When used in conjunction with several state-of-the-art
algorithms, this further increases their already good performance. Furthermore,
we propose an unsupervised scheme that yields almost similar improvements
without the need for ground truth.
