Moving object detection based on confidence factor and CSLBP features

Moving object detection is a challenging task in many computer vision applications. In this paper, we propose a robust background modelling method for this task. First, the background is updated by an adaptive strategy based on Centre-symmetric Local Binary Patterns. Then, background subtraction is used for detecting moving object. Although the traditional background subtraction technique uses the difference value between the current pixel and its corresponding background pixel for objection detection, our method utilises the confidence factor to determine whether the current pixel is a background or foreground pixel. The confidence factor of the current pixel is calculated in term of the difference values of its neighbourhood pixels. In the experiments, the proposed algorithm is tested on several challenging datasets such as PETS 2009, BMC 2012 and SABS. The experimental results demonstrate that our algorithm can robustly detect moving object under various scenes.