Background Subtraction Based on Multi-channel SILTP

Background subtraction is the first step in many video surveillance systems, its performance has a decisive influence on the result of the post-processing. An effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly, such as illumination changes or dynamic scenes. In this paper, a novel pixel-wise background subtraction algorithm is introduced. First, we propose a novel texture descriptor named Multi-Channel Scale Invariant Local Ternary Pattern(MC-SILTP). The pattern is cross-calculated in RGB color channels with the Scale Invariant Local Ternary Pattern operator. This descriptor does not only show an excellent performance in abundant texture regions, but also in flat regions. Secondly, we model each background pixel with a codebook rather than estimating the probability density functions. The codebook is consisted of many MC-SILTP samples actually observed in the past. A lot of experiments have been done over the proposed approach, results indicates that this approach is well balanced in sensitivity and robustness. It can handle the tricky problem of illumination changes robustly while detecting complete objects in flat areas sensitively. Comparison between the proposed one and several popular background subtraction algorithms demonstrates that it outperforms the state-of-the-art.