Splitting Gaussians in Mixture Models

Ruben Heras Evangelio, Michael Patzold, Thomas Sikora
2012 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance  
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
more » ... n 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.
doi:10.1109/avss.2012.69 dblp:conf/avss/EvangelioPS12 fatcat:3dsqcp2l7jfkrkcp6unzacdioi