A Novel Clustering-Based Method for Adaptive Background Segmentation
This paper presents a new histogram-based method for dynamic background modeling using a sequence of images extracted from video. In particular, a k-means clustering technique has been used to identify the foreground objects. Because of its shadow resistance and discriminative properties, we have used images in the HSV color space instead of the traditional RGB color space. The experimental results on real images are very encouraging as we were able to retrieve perfect backgrounds in simple scenes. In very complex scenes, the backgrounds we have obtained were very good. Furthermore, our method is very fast and could be used in real-time applications after optimization.