Video Object Segmentation by Fusion of Spatio-Temporal Information Based on Gaussian Mixture Model

In the field of surveillance, Effective and rapid video object segmentation is a key technologies for video analysis and processing. For the complex scene and noise that affecting segmentation issue in the fixed occasion. On the base of classic and adaptive Gaussian Mixture Background Model presented by Stauffer. In this paper, A new algorithm named the fusion of Spatio-Temporal based on Gaussian Mixture Model is proposed for video object segmentation. The algorithm classifies for each pixel in Time and Space scales. Firstly, the algorithm constructs dynamically Gaussian Mixture Background Model for each pixel and segment foreground objects through background subtraction. Secondly, the algorithm detects synchronously the neighborhood statistic feature of each pixel through two lemmas. Finally, producing a result using the spatial segmentation coupling with the temporal segmentation by “and” operator. Experiments show that the algorithm can segment the moving object effectively and quickly from video sequences and has stronger robustness application prospect contrasted with other algorithms.