3D HUMAN POSE ESTIMATION USING STOCHASTIC OPTIMIZATION IN REAL TIME

Random Tree Walkers (RTW) are a well-established method for human pose estimation, because they deliver state-of-the-art performance at low computational cost. As the forests capabilities for generalization are limited, the algorithm fails to estimate unlearned poses very quickly. The proposed method pushes this limitation by combining the RTW with optimization methods such as iterative closest point (ICP) and a stochastic search. The RTW is being used to initialize various hypotheses in different ways which are then passed to the optimization stage of the proposed method. The quality of each hypothesis is assessed by a cost function measuring the discrepancy between the data and a human body model generated for each hypothesis. Experimental results show a greater number of correctly estimated poses over a single RTW result.