Department of Computer Science & Engineering

We address the problem of tracking multiple, identical, nonrigid moving targets through occlusion for purposes of rodent surveillance from a side view. Automated behavior analysis of individual mice promises to improve animal care and data collection in medical research. In our experiments, we consider the case of three brown mice that repeatedly occlude one another and have no stable trackable features. Our proposed algorithm computes and incorporates a hint of the future location of the target into layer-based ane optical flow estimation. The hint is based on the estimated correspondences between mice in different frames derived from a depth ordering heuristic. Our approach is simple, ecient, and does not require a manually constructed mouse template. We demonstrate encouraging results on a challenging test sequence containing multiple instances of severe occlusion.

Pre-2018 CSE ID: CS2003-0763