Splitting Gaussians in Mixture Models

Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements. Nevertheless, they still often suffer from the problem of converging to poor solutions if the main mode stretches and thus over-dominates weaker distributions. Based on the results of the Split and Merge EM algorithm, in this paper we propose a solution to this problem. Therefore, we define an appropriate splitting operation and the corresponding criterion for the selection of candidate modes, for the case of background subtraction. The proposed method achieves better background models than state-of-the-art approaches and is low demanding in terms of processing time and memory requirements, therefore making it especially appealing in the surveillance domain.