Spatial-temporal nonparametric background subtraction in dynamic scenes

Traditional background subtraction methods model only tem-poral variation of each pixel. However, there is also spatial variation in real word due to dynamic background such as waving trees, spouting fountain and camera jitters, which causes the significant performance degradation of traditional methods. In this paper, a novel spatial-temporal nonparamet-ric background subtraction approach (STNBS) is proposed to effectively handle dynamic background by modeling the spatial and temporal variations simultaneously. Specially, for each pixel in an image, we adaptively maintain a sample consisting of pixels observed in previous frames. At current frame, for a particular pixel, the proposed method estimates the probabilities of observing this pixel based on samples of its neighboring pixels. The pixel is labeled as background if one of these estimated probabilities is larger than a fixed threshold. All samples are adaptively updated over time. Experimental results on several challenging sequences show that the proposed method achieves the best performance than two state-of-the-art algorithms. Index Terms — Background modeling, moving objects detection, Gaussian mixture model, kernel density estimatio