Statistical feature bag based background subtraction for local change detection.

A novel biunique background subtraction (BGS) technique is proposed.An efficient combination of six local features are used for this.In background training, the multi-valued background pixels are collected.For subtraction, each pixel is compared with those of the computed parameters.Majority voting technique is used to classify a pixel as object or background. This article proposes a novel background subtraction (BGS) technique to detect local changes corresponding to the movement of the objects in video scenes. Here we propose an efficient combination of six local features; three existing and three newly proposed. For background modeling and subtraction here a statistical parametric biunique model is proposed. In the proposed BGS scheme, during the background training phase, the multi-valued features corresponding to background pixels are collected. A few simple statistical parameters are used to characterize each feature. For background subtraction, the multi-valued features computed at each pixel location are compared with those of the computed parameters corresponding to that feature. For each pixel location, different labels (either object or background) are obtained due to different features. For assigning a final label to the pixel in the target frame a majority voting based label fusion technique is used. The proposed technique is successfully tested over several video sequences and found to be providing better results compared to various existing state-of-the-art techniques with three performance evaluation measures.