# Robust Background Subtraction via the Local Similarity Statistical Descriptor
## 3 Background Modeling
### Foreground Detection
In Table 5, we give a detail per-category average F-Measure comparisons between our method and the LOBSTER method. In the camera jitter and dynamic background categories, the F-Measure scores of our method are much higher than for LOBSTER. In particular, in the dynamic background category, our method shows an amazing 31.4% increase. Again, this demonstrates that the LSSD texture feature not only has the advantage of dealing with illumination variation and camouflage problems as most other texture features do, but also overcomes the disadvantage of noise sensitivity, which makes our method more robust in realistic difficult environmental conditions, like complex background motion and camera vibration. Finally, we present some qualitative comparisons between LOBSTER, ViBe, GMM, and our method with respect to the CDnet2012 dataset. As shown in Figure 11, we chose six sequences from each category. Each row exhibits a comparison among these methods; from top to bottom they are highway (baseline), traffic (camera jitter), fall (dynamic background), sofa (intermittent object motion), \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Category** & **Recall** & **Specificity** & **FPR** & **FNR** & **PWC** & **Precision** & **F-Measure** \\ \hline baseline & 0.9512 & 0.9975 & 0.0025 & 0.0488 & 0.4199 & 0.9217 & 0.9361 \\ cameraJ & 0.8760 & 0.9776 & 0.0224 & 0.1240 & 2.6496 & 0.7303 & 0.7803 \\ dynamic & 0.8905 & 0.9960 & 0.0040 & 0.1095 & 0.5141 & 0.7126 & 0.7442 \\ intermittent & 0.7202 & 0.9242 & 0.0758 & 0.2798 & 8.8154 & 0.6053 & 0.6094 \\ shadow & 0.9398 & 0.9903 & 0.0097 & 0.0602 & 1.1778 & 0.8148 & 0.8714 \\ thermal & 0.7878 & 0.9945 & 0.0054 & 0.2122 & 1.3138 & 0.8776 & 0.8134 \\ \hline Overall & 0.8609 & 0.9800 & 0.0199 & 0.1390 & 2.4817 & 0.7771 & 0.7924 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Complete results obtained with the proposed method on the CDnet2012 dataset. FPR: false positive rate; FNR: false negative rate; PWC: percentage of wrong classificationscopyMachine (shadow) and library (thermal). The first column is the input image from difference sequences, the second column represents the corresponding ground-truth, the third column shows the segmentation results of our method, the fourth column represents the segmentation results of the LOBSTER [26], the fifth column is the segmentation results of the ViBe [20] and the last column shows the output of the GMM [4] segmentation. Several visual conclusions can be obtained from observing these images. In most cases, our LSSD methodachieves better segmentation results than the other alternatives. In the highway sequence of baseline, the foreground segmentation results of our algorithm are perfect, almost the same as with the ground-truth. In the traffic and fall sequences with camera vibration and dynamic background, unlike the other methods, repetitive movements of the background objects are implicitly avoided in our background model, especially in the traffic sequence, where the highway fence is segmented as foreground in the remaining methods. In the fall sequence, few tree leaves are considered as foreground in our method, and thus the robustness of the LSSD texture feature may be of benefit. A camouflage problem, in which there are significant similarities between the background colors and foreground colors causing holes in the segmentation results, is also shown in several sequences, such as sofa and copyMachine. In the sofa sequence, the color of the man's trousers is similar to the color of the sofa. Even humans find it hard to segment them accurately, while our method obtains a concatenate foreground mask. Meanwhile, holes are detected in the results for LOBSTER and ViBe, resulting in the foreground object being divided into several parts. It is also amazing that the GMM achieved blank results. This may be because, as time goes by, the foreground object is fully absorbed into the background model.