Evaluating Recognition-Based Motion Capture on HumanEva II Test Data

The advent of the HumanEva standardized motion capture data sets has enabled quantitative evaluation of motion capture algorithms on comparable terms. This paper measures the performance of an existing monocular recognition-based pose recovery algorithm on select HumanEva data, including all the HumanEva II clips. The method uses a physically-motivated Markov process to connect adajacent frames and achieve a 3D relative mean error of 8.9 cm per joint, better than recently reported results. It further investigates factors contributing to the error, and finds that research into better pose retrieval methods offers promise for improvement of this technique and those related to it. Finally, it investigates the effects of local search optimization with the same recognition-based algorithm and finds no significant deterioration in the results, indicating that processing speed can be largely independent of the size of the recognition library for this approach.

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