Color cues in Human Motion Analysis

Abstract

An important part in the analysis of human activity video data is the silhouette segmentation. The results of segmentation are greatly affected by imaging environment, thus posing a problem for the extraction of the region in the image containing a subject of interest. In this paper we propose a human motion analysis method based on nonparametric clustering of monocular color images. Motivated by the need to automatically extract human silhouettes for kinematic gait analysis, without spurious segmentations naturally occurring in other segmentation methods, we have developed and applied adaptive mesh-based color clustering which can be combined with motion segmentation. The advantage of our method is that it is controlled by few intuitive parameters allowing the method to be adjusted to different capturing environments. The usage of color is not sensitive to illumination variations, or to different color distributions of different cameras, as the color distribution is compared between different regions of the same frame. To provide a qualitative evaluation of our method, our results are compared with the Gaussian Mixture model, a standard vision-based human subject extraction method based on background segmentation techniques.