Towards Model-Based Recognition of Human Movements in Image Sequences

Abstract

The interpretation of the movements of articulated bodies in image sequences is one of the most challenging problems in computer vision. In this contribution, we introduce a model-based approach for the recognition of pedestrians. We represent the human body by a 3D-model consisting of cylinders, whereas for modelling the movement of walking we use data from medical motion studies. The estimation of model parameters in consecutive images is done by applying a Kalman filter. Experimental results are shown for synthetic as well as for real image data.