Real-time 3-D human body tracking using learnt models of behaviour
In this paper, we introduce a 3-D human-body tracker capable of handling fast and complex motions in real-time. We build upon the MonteCarlo Bayesian framework, and propose novel prediction and evaluation methods improving the robustness and efficiency of the tracker. The parameter space, augmented with first order derivatives, is automatically partitioned into Gaussian clusters each representing an elementary motion: hypothesis propagation inside each cluster is therefore accurate and efficient. The transitions between clusters use the predictions of a variable length Markov model which can explain high-level behaviours over a long history. Using MonteCarlo methods, evaluation of model candidates is critical for both speed and robustness. We present a new evaluation scheme based on hierarchical 3-D reconstruction and blob-fitting, where appearance models and image evidences are represented by mixtures of Gaussian blobs. Our tracker is also capable of automatic-initialisation and self-recovery. We demonstrate the application of our tracker to long video sequences exhibiting rapid and diverse movements.
real-time montecarlo bayesian variable length markov models blobs human-body tracking volumetric reconstruction visual-hull cross-entropy kullbackleibler