Foreground object detection based on multi-model background maintenance

This paper addresses the problem of background maintenance for foreground object detection. A Multi-model Background Maintenance (MBM) framework that contains two principal features is proposed. Under this framework, a pure time-varying background image is maintained and learned using the statistical information of the multi-model Gaussian distribution with principle features. The principal features consist of static and dynamic pixels to represent the characteristic of background. Experiments are conducted on ten image sequences containing targets of interest in a variety of environments. Quantitative evaluation and comparison with the existing method show that the proposed method provides much improved results.