|Conference/Proceedings||9th IEEE International Conference on Advanced Video and Signal-Based Surveillance|
|Author(s)||Rubén Heras Evangelio, Michael Pätzold, Thomas Sikora|
|Title||Splitting Gaussians in Mixture Models|
|Abstract||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.
|Key words||Background subtraction, Gaussian mixture models, video surveillance, BGSubLib|