Analyzing Motion Parameters Using Unsupervised Fuzzy C-Prototypes

Abstract

Motion-based segmentation plays an important role in dynamic scene analysis of video sequence. This technique should not only cluster the feature vectors but also extract the optimum number of clusters that correspond to the moving objects. The motion features of moving objects in a video sequence have to be extracted so that segmentation can be performed based on this information. In this paper, we present a scheme for extracting moving objects. First, the dense optical flow fields are calculated to extract motion vectors. Surface fitting is performed over the parametric motion model. Then, an unsupervised robust fuzzy C-Prototypes clustering technique is applied to motion-based segmentation in the parameter space. Finally, the individual moving object and background can be represented in layers. Experimental results showing the significance of ths proposed method are provided. 1 Introduction Recent technology in digital video processing has moved to "content-based" storage and retr..