Adaptive Free Cylindrical Mixture Model for Foreground Estimation in Rapidly Fluctuating Dynamic Background Conditions

A novel method of modelling pixel distributions for foreground detection in rapidly fluctuating dynamic background conditions is presented in this paper. A comprehensive study of the characteristics of pixel behaviour in videos of backgrounds in clear water under natural lighting conditions has been presented in this work. Videos from real world situations such as in swimming pools and ponds where foreground detection is important were analyzed and it was identified that the distributions of pixel intensity values in a single pixel appear to form cylindrical clusters in RGB space. Therefore, in order to model the highly dynamic rapidly fluctuating background scenes in aquatic conditions, a novel cylindrical model is proposed where the axis is freed to allow for the high dynamism. An adaptive free cylindrical mixture model (AFCMM), which learns the directions of orientation of the clusters using an eigenanalysis based approach, is proposed for foreground detection in aquatic conditions. The results from foreground estimation in a swimming pool using the adaptive Gaussian mixture model and the proposed AFCMM have been compared and it has been shown that the latter provides an improved estimate of the foreground while demonstrating its effectiveness as a better descriptor for the pixel dynamics under such conditions.