# Robust Background Subtraction via the Local Similarity Statistical Descriptor
## 1 Introduction
[11] proposed an improved codebook by incorporating the spatial-temporal context of each pixel. Unprecedented background subtraction methods based on neural networks have been proposed in [12; 13] and achieve good results on various scenarios. The Self-Organizing Background Subtraction (SOBS) algorithm models each pixel with a neural map of weight vectors. Moving objects are detected through a map of motion and stationary patterns. The background model update at each pixel location is influenced by the labeling decision of its neighbors. As can be seen, more and more recent methods tend to account for neighboring pixels to add robustness to noise. For examples, superpixels and Markov Random Fields [14], as well as the connected components [15] focus on improving label coherence using advanced regularization techniques. Some other methods rely on the region level [16; 17], frame level [18] or hybrid frame-region level [19]. The first non-deterministic background subtraction method, called ViBe, was proposed in [20] and has been shown to outperform many existing methods. Instead of building the probability distribution of the background for each pixel using a Parzen window, ViBe uses a stochastic maintenance strategy to integrate new information into the model. If the pixel in the new frame matches some of the background samples, it is classified as background and has a probability of being inserted into the sample model at the corresponding pixel location. The authors show that the stochastic strategy ensures a smooth, exponentially decaying lifespan for the samples that constitute the pixel models. In order to maintain spatial consistency, a spatial information propagation strategy randomly diffuses pixel values across neighboring pixels, even the ones marked as foreground. Due to its simplicity and effectiveness, we use a similar model update strategy in our background subtraction framework. As more effective models appear, more powerful feature representations are also developed to better adapt to challenging situations. These features include color features, edge features, stereo features, motion features and texture features. The Local Binary Pattern (LBP) feature [21] is the first texture feature proposed for background subtraction. Each pixel is modeled as a group of LBP histograms calculated over its neighborhoods. This method was demonstrated to be tolerant to illumination variations and robust against multimodal background regions, but at the expense of sensitivity to subtle local texture changes. An improved version of LBP called the Scale-Invariant Local Ternary Pattern (SILTP) was proposed in [22], which exceeds LBP in computational efficiency and tolerance to noises. Recently, Local Binary Similarity Patterns were proposed in [23], based on absolute difference, and were demonstrated to surpass traditional color comparisons via Hamming distance thresholding. Despite the fact that both of them are robust to illumination variations, they perform poorly in flat areas and result in \"holes\" in objects. Then, some researchers began to combine different features to benefit from eachother. For example, [PERSON] et al. [24] proposed a multi-layer background model based on color features and texture features. [PERSON] et al. [25] proposed a background subtraction method using a Support Vector Machine over background likelihood vectors for a set of features which consist of color, gradient, and Haar-like features. More recently, [PERSON] et al. [26] performed the background subtraction by integrating the color features and the Local Binary Similarity Pattern (LBSP) features, and showed state-of-the-art performance. In this paper, we present a robust background subtraction method which combines color features and texture features to characterize pixel representations. Our contributions lie in three aspects.