An evolutionary approach for pose determination and interpretation of occluded articulated objects

This paper proposes a novel evolutionary approach to a parameter solving problem for handling occluded articulated objects with any number of internal parameters representing articulation. The parameter solving problem is formulated as a parameter optimization problem and an objective function is also given based on the line segment features in the Hough space. The proposed approach uses a novel intelligent genetic algorithm (IGA) superior to conventional GAs in solving large parameter optimization problems to simultaneously solve pose determination and interpretation problems and consequently has the capabilities of accurate partial matching and robust pose determination. Effectiveness of the proposed IGA-based method is demonstrated by applying it to fitting a simplified artificial articulated model of a human body to monocular clutter images