# Robust Background Subtraction via the Local Similarity Statistical Descriptor
## 3 Background Modeling
### Foreground Detection
Nevertheless, for some applications, parameters can be fine-tuned for some specific needs. Overall, the six parameters, detailed below, were tuned in the dataset. The performance with different parameter settings is shown in Figure 9.

* \(N=45\): the number of samples stored in the background model for each pixel. * \(\#_{min}=2\): minimum number of sample matches to label an input pixel as background. An optimum is found at \(\#_{min}=2\). * \(R_{c}=15\): color distance threshold to determine whether an input pixel matches the background sample. * \((r,c)=(5\times 5)\): neighborhood window size to calculate LSSD features in Equation (7). * \(\tau=0.18\): interval factor used to calculate LSSD features in Equation (8). * \(\delta=0.20\): parameter factor used to calculate the LSSD distance threshold \(R_{t}\) in Equation (15). In our proposed method, the classification decision is made independently for each pixel. The foreground detection result can benefit from a regularization step, which combines information from neighboring pixels and assigns homogeneous labels on uniform regions. In preliminary experiments, simple median filtering provided superior results to morphological operations. Thus, we decided to use a median filter for post-processing. In this paper, we use a uniform \(7\times 7\) median filter for all evaluated methods. In practice, the input image is a three-channel color image; we process each channel independently and run them in three parallel threads. The final segmentation result is the bitwise OR operation of the three segmentation results from \(RGB\) channels. ### Performance Evaluation

Firstly, to demonstrate our key contribution, the LSSD texture feature is shown to be preferable to other texture features (LBP [21], SILTP [22], and LBSP [26]). In Table 2, we present the performance comparison of different features with respect to the the CDnet2012 dataset. Here, we can see that the LSSD feature obtains a much higher F-Measure score than LBP and SILTP. This may explain that although LBP and SILTP can detect texture variation easily, they are too sensitive to the noise and dynamic background in some scenarios, resulting in poor overall performance. We also see that the LBSP feature obtains much higher F-Measure score than the other texture features. This is the benefit obtained from combining temporal and spatial information to describe texture changes. Others only take spatial information to calculate the features. Although LBSP obtains the highest F-Measure score, the LBSP feature is more sensitive to the noise and dynamic background. Figure 9: F-Measure of our method on each category as well as overall performance with changing parameter settings. (CDnet2012 dataset). score when only taking the texture features, we will demonstrate that when integrated with the color features, our method performs much better than all others. Secondly, to demonstrate the effectiveness of the combination of color features and texture features, we select the two typical sequences office and boat. The clothes in the office include flat texture, which is very similar to the texture of the wall; the boat sequence contains the complex background of a rippled water surface. Figure 10 shows the experimental results when only using color features (the second column), only using texture features (the third column) and using color features and texture features (the fourth column). For the office sequences, the color information can well distinguish the person and the wall, but only using the texture features will result in missed detection, resulting in \"holes\" in objects. For the boat sequences, which contain the movement of water, only using the color features will generate false detection. However, using the LSSD texture features will suppress these false detections. The experimental results in Figure 10 demonstrate that the color features and the LSSD features have their own merits and demerits. They can compensate for each other to obtain a better segmentation result.