## 2 The Proposed Method
As an example, for an input image of \(320\times 240\) in size, the memory required to extract the convolution feature from the input image by applying the conv1_1 layer of the VGG16 is \(320\times 240\times 64=4.9\) M, and the weight is \(3\times 3\times 3\times 64=1728\). ## 4 Conclusions

In this paper, we present an unsupervised, simple and universal framework that takes advantage of lower layer features of pretrained CNN for refinements of dynamic background subtraction methods. The framework extracts the low-level features of input images and merges the representation features of the input image into a new feature image. The background subtraction methods produce more accurate foreground object detection results by modeling the key features. To verify the performance of the proposed method, the dynamic background database was used to carry out experiments. Experiments showed that the proposed method significantly refined the traditional background subtraction methods for dynamic scenes. [PERSON] contributed to the paper writing and revision. [PERSON] and [PERSON] made some supplements and revision. All authors have read and approved the final manuscript. This research was supported by the Natural Science Foundation of China (61876029). The authors declare no conflict of interest. ## References

* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON] An efficient low cost background subtraction method to extract foreground object during human tracking. In Proceedings of the International Conference on Circuits, Power and Computing Technologies, Nagercoil, India, 20-21 March 2014; pp. 1432-1436. * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] Moving object detection by detecting contiguous outliers in the low-rank representation. _IEEE Trans. Pattern Anal. Mach. Intell._**2013**, _35_, 597-610. [CrossRef] [PubMed]
* [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Adaptive multi-cue background subtraction for robust vehicle counting and classification. _IEEE Trans. Intell. Transp. Syst._**2012**, _13_, 527-540. [CrossRef]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] Algorithm and architecture design of human-machine interaction in foreground object detection with dynamic scene. _IEEE Trans. Circuits Syst. Video Technol._**2013**, _23_, 15-29. [CrossRef] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Videos** & **Methods** & **Recall** & **FPR** & **FNR** & **PBC** & **Precision** & **F-Measure** \\ \hline boats & GMM & 0.5418 & 0.0627 & 0.4582 & 6.5735 & 0.0635 & 0.1136 \\  & GMM\({}_{\text{PSNR}}\) & 0.5563 & 0.0264 & 0.4437 & 2.9650 & 0.1417 & 0.2259 \\  & GMM\({}_{\text{SSIM}}\) & 0.3483 & 0.0272 & 0.6517 & 3.1088 & 0.0749 & 0.1232 \\ \hline canoe & GMM & 0.5762 & 0.0673 & 0.4238 & 8.2353 & 0.2734 & 0.3708 \\  & GMM\({}_{\text{PSNR}}\) & 0.6212 & 0.0261 & 0.3788 & 4.1436 & 0.5190 & 0.5655 \\  & GMM\({}_{\text{SSIM}}\) & 0.4163 & 0.0474 & 0.5837 & 6.6359 & 0.2439 & 0.3076 \\ \hline fall & GMM & 0.8427 & 0.0830 & 0.1573 & 8.4436 & 0.1638 & 0.2744 \\  & GMM\({}_{\text{PSNR}}\) & 0.8513 & 0.0553 & 0.1487 & 5.7039 & 0.2306 & 0.3629 \\  & GMM\({}_{\text{SSIM}}\) & 0.7391 & 0.0482 & 0.2609 & 5.1936 & 0.2168 & 0.3352 \\ \hline fountain01 & GMM & 0.8934 & 0.0274 & 0.1066 & 2.7464 & 0.0375 & 0.0721 \\  & GMM\({}_{\text{PSNR}}\) & 0.9096 & 0.0211 & 0.0904 & 2.1222 & 0.0506 & 0.0958 \\  & GMM\({}_{\text{SSIM}}\) & 0.7835 & 0.0168 & 0.2165 & 1.7016 & 0.0372 & 0.0710 \\ \hline fountain02 & GMM & 0.9162 & 0.0058 & 0.0838 & 0.6076 & 0.3173 & 0.4713 \\  & GMM\({}_{\text{PSNR}}\) & 0.9031 & 0.0026 & 0.0969 & 0.2856 & 0.5251 & 0.6641 \\  & GMM\({}_{\text{SSIM}}\) & 0.7211 & 0.0021 & 0.2789 & 0.2732 & 0.4212 & 0.5318 \\ \hline overpass & GMM & 0.7704 & 0.0178 & 0.2296 & 2.1093 & 0.4100 & 0.5352 \\  & GMM\({}_{\text{PSNR}}\) & 0.7686 & 0.0077 & 0.2314 & 1.1307 & 0.6237 & 0.6886 \\  & GMM\({}_{\text{SSIM}}\) & 0.4666 & 0.0108 & 0.5334 & 1.7817 & 0.3694 & 0.4124 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparisons of PSNR and SSIM for GMM on dynamic scenes.