Human body pose estimation using silhouette shape analysis

We describe a system for human body pose estimation from multiple views that is fast and completely automatic. The algorithm works in the presence of multiple people by decoupling the problems of pose estimation of different people. The pose is estimated based on a likelihood function that integrates information from multiple views and thus obtains a globally optimal solution. Other characteristics that make our method more general than previous work include: (1) no manual initialization; (2) no specification of the dimensions of the 3D structure; (3) no reliance on some learned poses or patterns of activity; (4) insensitivity to edges and clutter in the background and within the foreground. The algorithm has applications in surveillance and promising results have been obtained.

1 Introduction

Determining the pose of humans is an important problem in vision and has many applications. We describe a system for human body pose estimation from multiple views that is suitable for surveillance applications. The emphasis is on building a system that is fast and general, rather than on very accurate recovery of the pose parameters under constrained scenarios.