A Robust Multiple Hypothesis Approach to Monocular Human Motion Tracking

We study the problem of articulated 3D human motion tracking in monocular video sequences. Addressing problems related to unconstrained scene structure, uncertainty, and the high-dimensional parameter spaces required for human modeling, we present a novel, layered-robust, multiple hypothesis algorithm for estimating the distribution of the model parameters and propagating it over time. We use cost function based on robust contour and image intensity descriptors in a multiple assignment data association scheme. Our mixed discrete/global and continuous/local search technique uses both informed sampling and continuous optimization. Its novel hypothesis generation and pruning strategy focuses attention on poorly constrained directions in which large parameter space deviations are most likely, thus adaptively tracking the complex cost surface produced by non-linear kinematics, perspecti- ve projection and data-association problems. We also address the issue of semi-automatic acquisition of initial model pose and proportions, and show experimental tracking results involving complex motions with significant background clutter and self-occlusion