Background modelling using discriminative motion representation

Abstract

Robustness is an important factor for background modelling on various scenarios. Current pixel-based adaptive segmentation method cannot effectively tackle diverse objects simultaneously. To address this problem, in this study, a background modelling method using discriminative motion representation is proposed. Instead of simple usage of intensity to construct the background model, the proposed method extracts a new local descriptor which uses a weighted combination of differential excitations for each pixel to enhance the discriminability of pixels. On the basis of this background model, different categories of objects can be quickly identified by a simple but effective classification rule and accurately be represented in background model by a smart selection of updating strategies. Therefore, the authors' background modelling method can generate complete representation for static objects and decrease false detection caused by dynamic background or illumination variations. Extensive experiments have been conducted to demonstrate that the proposed method obtains more advantages of foreground detection than the state-of-the-art methods. In addition, the proposed method provides a computational efficient algorithm for foreground detection tasks.

WePBAS: A Weighted Pixel-Based Adaptive Segmenter for Change Detection

“…On the CDnet2014 dataset, we present the comparison of WePBAS, PBAS [14], GMM-Zivkovic [8], GMM-Stauffer and Grimson [7], ViBe [12], SBBS [25], and Zhong2017 [17] (see Table 3). Since the ViBe method and the PBAS method have not published the experimental results on CDnet2014, we use the test results of the two methods on the CDnet2014 dataset which can be got in the paper [17] and the paper [26]. Other results are derived from the original paper or the results published by the authors on the web.…”