Matching a Human Walking Sequence with a VRML Synthetic Model

In this paper we present a specific matching technique based on basic motor patterns between two image sequences taken from different view points and a VRML synthetic human model. The criteria used are part of a generic system for the analysis and synthesis of human motion. The system has two phases: an automatic or interactively supervised analysis phase where the motion is interpreted and a synthesis phase where the original motion is applied to a biomechanical model. The main goal of our approach consists of finding a general solution that could be applied to general motor patterns. We define a set of matching conditions and we describe general-purpose criteria in order to reduce the space of search. The complexity of human motion analysis has led researchers to study new approaches and design more robust techniques for human tracking, detection or reconstruction. Whereas mathematical solutions partially solve this problem, the complexity of the algorithms proposed only serve to limit these solutions for real time purposes or general kind of motion types considered. So, we propose more simple, less general approaches but with a low computational cost. In this case the human model information about the kind of movement to be studied is very important in the process of matching between key-frame images. We also try to develop a system that can, at least in part, overcome the limitations of view dependence.