Comparing evolutionary algorithms and particle filters for Markerless Human Motion Capture
Evolutionary algorithms and particle filters are applied to Markerless Motion Capture. Two different body models are proposed and analyzed in the dataset HumanEva-I. Two approaches are proposed for the search strategy: holistic and hierarchical. Non-parametric tests show that evolutionary algorithms outperform particle filters. Evolutionary algorithms and hierarchical strategies deal better with high dimensionality.
evolutionary algorithms particle filters markerless motion capture tracking