# Robust Background Subtraction via the Local Similarity Statistical Descriptor
## 3 Background Modeling
### Foreground Detection
In Table 2, we also present the results obtained when combining the color features and the LSSD features. According to the F-Measure scores, we can see that all texture features obtain performance improvement when combined with the color features; our method surpasses the LBSP and achieves the highest improvement. This is due to the robustness of the LSSD feature as compared with other intrinsic noise-sensitive texture features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Recall** & **Precision** & **F-Measure** \\ \hline LBP & 0.5462 & 0.3179 & 0.3748 \\ SILTP & 0.6140 & 0.4121 & 0.4232 \\ LSSD & 0.6673 & 0.5224 & 0.5545 \\ LBSP & 0.6262 & 0.8579 & 0.6841 \\ color & 0.6681 & 0.8426 & 0.7074 \\ \hline color + LBP & 0.7820 & 0.7470 & 0.7238 \\ color + SILTP & 0.8010 & 0.7521 & 0.7352 \\ color + LBSP & 0.7658 & 0.8016 & 0.7481 \\ color + LSSD & 0.8609 & 0.7771 & 0.7924 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average performance comparison of different features on the CDnet2012 dataset. LBP: Local Binary Pattern; SILTP: Scale-Invariant Local Ternary Pattern; LSSD: Local Similarity Statistical Descriptor; LBSP: Local Binary Similarity Pattern. Figure 10: Experimental results with different features on the sequences of office and boat. First column: original images. Second column: only using color features. Third column: only using LSSD features. Fourth column: combined color and LSSD features. Fifth column: the corresponding ground-truth. Gray pixels in the ground truth indicate pixels which are not of interest. Thirdly, we present the complete results of our method using the evaluation framework of the CDnet2012 dataset [28]. As shown in Table 3, we can see that our method performs well on the dataset with a overall F-Measure of 0.7924. In the baseline and shadow categories, one of the F-Measure scores is 0.9361 and the other is 0.8714. Both of the recall metric scores exceed 0.9. These two categories mainly consist of sequences where cars and pedestrians are the main focus with challenges like illumination variation and camouflage. We also see that the camera jitter and dynamic background categories are well handled by our method; the F-Measure and precision scores are all above 0.7. The same can be said for the thermal sequences, as the precision metric gets a high score of 0.8776. However, we also notice that the intermittent object motion category poses the greatest challenge. This category mainly consists of sequences with abandoned objects and parked cars that suddenly start moving. The main challenge involves static object detection, but it is not what most background subtraction methods are good at. Fourthly, in Table 4 we show how our method compared with some of the state-of-the-art methods. Due to a lack of space, we only chose seven classic methods: GMM [4], KDE [7], ViBe [20], SOBS [12], PSR-MRF [14], PBAS [33] and LOBSTER [26]. Among them, GMM, KDE, SOBS, PBAS and ViBe are pixel-level methods. PSR-MRF is region-level method. LOBSTER and our method are hybrid methods. The results of other methods are from the website www.changedetection.net. For a specific metric, if the method obtains a best score on it, the corresponding value is highlighted in bold. From Table 4 we can see that the ViBe has the best precision performance, LOBSTER has the best specificity performance, PBAS has the best performance on FPR and PWC metrics, and our method is the best in three out of seven metrics: recall, FNR and F-Measure. Of note, the F-Measure score is 0.7924, which is much higher than for all other methods. The LOBSTER method is very similar to our method. Both of them are hybrid methods, which combine pixel-level and region-level analyses. The LOBSTER presents a modified spatio-temporal binary texture descriptor derived from the Local Binary Similarity Pattern (LBSP) and results in a dramatic performance increase. However, as we concluded previously, the local binary pattern texture feature is noise-sensitive.