# Robust Background Subtraction via the Local Similarity Statistical Descriptor
## 3 Background Modeling
### Foreground Detection
For a pixel \(p\), each background sample requires one byte of memory to store the intensity information and one byte of memory to store the LSSD information per channel. According to the parameters set in our experiment described above, each pixel background model contains \(N=45\) background samples. Then, for a color sequence with frame size of \(720\times 576\) (e.g., PETS2006), the memory requirement of our method would be about 300 MB. This is consistent with the results obtained by the Visual Studio 2015 performance analysis tool. For an embedded platform, decreasing the number of background samples can dramatically reduce the memory usage. ## 5 Conclusions

In this paper, we first propose a novel texture feature named the Local Similarity Statistical Descriptor (LSSD), which shows good performance in illumination variation and dynamic background scenes. Then, we show that the color features and the texture features have their own merits and demerits. A combination of color features and LSSD features results in a compensation of defects, resulting in a dramatic performance increase. Experiments on the CDnet2012 dataset have shown that in the metric of F-Measure, our method outperforms many recent state-of-the-art background subtraction methods. A number of improvements can also be considered for our method. In future works, we will integrate our framework with more a complex feedback model update strategy (such as [31; 33]). Region-level analyses could also be used to improve the foreground object consistency, and more sophisticated post-processing operations like the Markov Random Field could also help refine our results. **Acknowledgments: This research is supported by the National Natural Science Foundation of China under Grant No.61401425. **

**Author Contributions: [PERSON] and [PERSON] performed the experiments and wrote the paper, [PERSON] helped to perform the experiments, [PERSON] and [PERSON] helped to wrote the paper. **

**Conflicts of Interest: The authors declare no conflict of interest. **

## References

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\begin{tabular}{c c c c} \hline \hline
**Sequence** & **Frame Size** & **LSSD (fps)** & **ViBe (fps)** \\ \hline highway & \(320\times 240\) & \(127\) & \(323\) \\ park & \(352\times 288\) & \(101\) & \(273\) \\ pedestrians & \(360\times 240\) & \(116\) & \(301\) \\ badmin & \(720\times 480\) & \(33\) & \(76\) \\ PETS2006 & \(720\times 576\) & \(26\) & \(63\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: LSSD and ViBe computational speed comparison in terms of frame per seconds (fps). * [PERSON] et al. (2007) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] A robust video foreground segmentation by using generalized gaussian mixture modeling. In Proceedings of the 2007 CRV'07 Fourth Canadian Conference on Computer and Robot Vision, Montreal, QC, Canada, 28-30 May 2007; IEEE: Piscataway, NJ, USA, 2007; pp. 503-509. * [PERSON] et al. (2002) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. _Proc. IEEE_**2002**, _90_, 1151-1163.