## 2 The Proposed Method
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7-12 June 2015; pp. 3431-3440. * [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] Background subtraction using multiscale fully convolutional network. _IEEE Access_**2018**, \(6\), 16010-16021. [CrossRef]
* [PERSON] (2017) [PERSON] Anomaly Detection in Surveillance Videos Using Deep Residual Networks. Ph.D. Thesis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil, 2017. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27-30 June 2016; pp. 770-778. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep background modeling using fully convolutional network. _IEEE Trans. Intell. Transp. Syst._**2018**, _19_, 254-262. [CrossRef]* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] Interactive deep learning method for segmenting moving objects. _Pattern Recognit. Lett._**2017**, _96_, 66-75. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] Foreground segmentation using a triplet convolutional neural network for multiscale feature encoding. _arXiv_**2018**, arXiv:1801.02225. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Adaptive deep convolutional neural networks for scene-specific object detection. _IEEE Trans. Circuits Syst. Video Technol._**2017**, _16_, 1519-1526. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep learning driven blockwise moving object detection with binary scene modeling. _Neurocomputing_**2015**, _168_, 454-463. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Background subtraction based on deep pixel distribution learning. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA, 23-27 July 2018. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] Background subtraction using encoder-decoder structured convolutional neural network. In Proceedings of the 14 th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy, 29 August-1 September 2017. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Background subtraction on depth videos with convolutional neural networks. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8-13 July 2018. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Combining Background Subtraction and Convolutional Neural Network for Anomaly Detection in Pumping-Unit Surveillance. _Algorithms_**2019**, _12_, 115. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Combining background subtraction algorithms with convolutional neural network. _J. Electron. Imaging_**2019**, _28_, 013011. [CrossRef]
* [PERSON] et al. (2014) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] How transferable are features in deep neural networks? In Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, 8-13 December 2014; pp. 3320-3328. * [PERSON] and [PERSON] (2014) [PERSON]; [PERSON] [PERSON] Very deep convolutional networks for largescale image recognition. _arXiv_**2014**, arXiv:1409.1556. * [PERSON] (2013) [PERSON] Bgslibrary: An opencv c++ background subtraction library. In Proceedings of the Ix Workshop De Visao Computacional, Rio de Janeiro, Brazil, 3-5 June 2013; p. 2. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Embedded motion detection via neural response mixture background modeling. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA, 26 June-1 July 2016; pp. 837-844. * [PERSON] and [PERSON] (2010) [PERSON]; [PERSON] Image quality metrics: PSNR vs. SSIM.