Human Pose Estimation using Dynamic Programming

Linus Svärm

The goal of this Master's thesis has been to develop a computationally efficient algorithm which is able to find the configuration of articulated objects. The focus of this work has been on recovering the pose of human bodies in three dimensions, but the method proposed is fairly general and can be used for a broad class of deformable objects. The method is based on the pictorial structure representation introduced by Fischler and Elschlager, where an object is represented by a set of parts connected by flexible joints. The parts encode visual properties of the object and the joints encode its deformations. The best match of such a model to an image is found by maximizing a likelihood function that measures both the match of each part and the deformation of their joints. The many degrees of freedom of the objects create very high-dimensional parameter spaces. However, by restricting the the model structure, the globally optimal configuration can be found efficiently by the use of dynamic programming and integral images. Results from using the method to find the pose of humans are presented.

• Populärvetenskaplig sammanfattning (Swedish)
• Report (pdf)