Hybrid Monte Carlo filtering: edge-based people tracking

Statistical inefficiency often limits the effectiveness of particle filters for high-dimensional Bayesian tracking problems. To improve sampling efficiency on continuous domains, we propose the use of a particle filter with hybrid Monte Carlo (HMC), an MCMC (Markov chain Monte Carlo) method that follows posterior gradients toward. high probability states, while ensuring a properly weighted approximation to the posterior. We use HMC filtering to infer the 3D shape and motion of people from natural, monocular image sequences. The approach currently uses an empirical, edge-based likelihood function, and a second-order dynamic model with soft biomechanical joint constraints.