Tech Report CS-03-08
Video-Based Tracking of 3D Human Motion Using Multiple Cameras
We present a system designed to track a human in three dimensions given video sequences from multiple cameras. The tracking problem is formulated as a Bayesian inference task, and we approximate the posterior distribution with a particle set. When many particles share very little weight, we find that tracking results suffer. We try to prevent such a circumstance by introducing a dynamic mapping from measurements to likelihoods. Additionally, we found that multiple cameras stabilized tracking in three dimensions by disambiguating limb locations in the depth dimension for any one specific camera.