Approximate Learning and Inference for Tracking with Non-overlapping Cameras
Wojciech Zajdel, Ben Kröse · UvA-DARE (University of Amsterdam) · 2003
Tracking with multiple cameras requires partitioning of observations from various sensors into trajectories. In this paper we assume that the observations are generated by a hidden, stochastic 'partition' process and propose a hidden Markov model (HMM) as a generative model for the data. The state space for the hidden variable is intractable, so the inference and learning in our HMM are based on approximate representation of the distribution on this state space. The proposed approximation truncates the distribution from unlikely states. We test our method on real observations; by tracking people in a university building. The tests show that the described approach is an useful alternative to the existing approximate methods.