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Abstract

Human motion capturing can be regarded as an optimization problem where one searches for the pose that minimizes a previously defined error function based on some image features. Most approaches for solving this problem use iterative methods like gradient descent approaches. They work quite well as long as they do not get distracted by local optima. We introduce a novel approach for global optimization that is suitable for the tasks as they occur during human motion capturing. We call the method interacting simulated annealing since it is based on an interacting particle system that converges to the global optimum similar to simulated annealing. We provide a detailed mathematical discussion that includes convergence results and annealing properties. Moreover, we give two examples that demonstrate possible applications of the algorithm, namely a global optimization problem and a multi-view human motion capturing task including segmentation, prediction, and prior knowledge. A quantative error analysis also indicates the performance and the robustness of the interacting simulated annealing algorithm.