{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T01:08:54Z","timestamp":1768784934390,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,8]],"date-time":"2018-06-08T00:00:00Z","timestamp":1528416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP16J02614"],"award-info":[{"award-number":["JP16J02614"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Deep neural network-based (DNN-based) background subtraction has demonstrated excellent performance for moving object detection. The DNN-based background subtraction automatically learns the background features from training images and outperforms conventional background modeling based on handcraft features. However, previous works fail to detail why DNNs work well for change detection. This discussion helps to understand the potential of DNNs in background subtraction and to improve DNNs. In this paper, we observe feature maps in all layers of a DNN used in our investigation directly. The DNN provides feature maps with the same resolution as that of the input image. These feature maps help to analyze DNN behaviors because feature maps and the input image can be simultaneously compared. Furthermore, we analyzed important filters for the detection accuracy by removing specific filters from the trained DNN. From the experiments, we found that the DNN consists of subtraction operations in convolutional layers and thresholding operations in bias layers and scene-specific filters are generated to suppress false positives from dynamic backgrounds. In addition, we discuss the characteristics and issues of the DNN based on our observation.<\/jats:p>","DOI":"10.3390\/jimaging4060078","type":"journal-article","created":{"date-parts":[[2018,6,8]],"date-time":"2018-06-08T11:19:31Z","timestamp":1528456771000},"page":"78","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Analytics of Deep Neural Network-Based Background Subtraction"],"prefix":"10.3390","volume":"4","author":[{"given":"Tsubasa","family":"Minematsu","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsushi","family":"Shimada","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hideaki","family":"Uchiyama","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rin-ichiro","family":"Taniguchi","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.cosrev.2014.04.001","article-title":"Traditional and recent approaches in background modeling for foreground detection: An overview","volume":"11","author":"Bouwmans","year":"2014","journal-title":"Comput. Sci. Rev."},{"key":"ref_2","unstructured":"Stauffer, C., and Grimson, W.E.L. (1999, January 23\u201325). Adaptive background mixture models for real-time tracking. Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Elgammal, A., Harwood, D., and Davis, L. (2000). Non-parametric model for background subtraction. Computer Vision-ECCV 2000, Springer.","DOI":"10.1007\/3-540-45053-X_48"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Heikkil\u00e4, M., Pietik\u00e4inen, M., and Heikkil\u00e4, J. (2004, January 7\u20139). A texture-based method for detecting moving objects. Proceedings of the British Machine Vision Conference (BMVC), Kingston, UK.","DOI":"10.5244\/C.18.21"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"198","DOI":"10.2197\/ipsjtcva.3.198","article-title":"Statistical Local Difference Pattern for Background Modeling","volume":"3","author":"Yoshinaga","year":"2011","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"St-Charles, P.L., Bilodeau, G.A., and Bergevin, R. (2014, January 23\u201328). Flexible background subtraction with self-balanced local sensitivity. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.67"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., and Ishwar, P. (2014, January 23\u201328). CDnet 2014: An expanded change detection benchmark dataset. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.126"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.neucom.2015.05.082","article-title":"Deep learning driven blockwise moving object detection with binary scene modeling","volume":"168","author":"Zhang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_9","unstructured":"Javad Shafiee, M., Siva, P., Fieguth, P., and Wong, A. (July, January 26). Embedded motion detection via neural response mixture background modeling. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Braham, M., and Van Droogenbroeck, M. (2016, January 23\u201325). Deep background subtraction with scene-specific convolutional neural networks. Proceedings of the 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia.","DOI":"10.1109\/IWSSIP.2016.7502717"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.patcog.2017.09.040","article-title":"A deep convolutional neural network for video sequence background subtraction","volume":"76","author":"Babaee","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lim, K., Jang, W.D., and Kim, C.S. (September, January 29). Background subtraction using encoder-decoder structured convolutional neural network. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078547"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Minematsu, T., Shimada, A., and Taniguchi, R.-i. (September, January 29). Analytics of deep neural network in change detection. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078550"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Barnich, O., and Van Droogenbroeck, M. (2009, January 19\u201324). ViBe: A powerful random technique to estimate the background in video sequences. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009), Taipei, Taiwan.","DOI":"10.1109\/ICASSP.2009.4959741"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.rti.2004.12.004","article-title":"Real-time foreground\u2013background segmentation using codebook model","volume":"11","author":"Kim","year":"2005","journal-title":"Real-Time Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jain, V., Kimia, B.B., and Mundy, J.L. (October, January 16). Background modeling based on subpixel edges. Proceedings of the IEEE International Conference on Image Processing (ICIP 2007), San Antonio, TX, USA.","DOI":"10.1109\/ICIP.2007.4379586"},{"key":"ref_17","first-page":"11","article-title":"Robust principal component analysis?","volume":"58","author":"Li","year":"2011","journal-title":"J. ACM (JACM)"},{"key":"ref_18","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2014, January 21\u201326). DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), Beijing, China."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1023\/A:1011139631724","article-title":"Modeling the shape of the scene: A holistic representation of the spatial envelope","volume":"42","author":"Oliva","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Braham, M., Pi\u00e9rard, S., and Van Droogenbroeck, M. (2017, January 17\u201320). Semantic background subtraction. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297144"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sakkos, D., Liu, H., Han, J., and Shao, L. (2017). End-to-end video background subtraction with 3d convolutional neural networks. Multimedia Tools Appl.","DOI":"10.1007\/s11042-017-5460-9"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 13\u201316). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.patrec.2016.09.014","article-title":"Interactive deep learning method for segmenting moving objects","volume":"96","author":"Wang","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and understanding convolutional networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mahendran, A., and Vedaldi, A. (2015, January 7\u201312). Understanding deep image representations by inverting them. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Binder, A., Bach, S., Montavon, G., M\u00fcller, K.R., and Samek, W. (2016). Layer-wise relevance propagation for deep neural network architectures. Information Science and Applications (ICISA) 2016, Springer.","DOI":"10.1007\/978-981-10-0557-2_87"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning With Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_32","unstructured":"Hinton, G., Srivastava, N., and Swersky, K. (2018, May 14). Neural Networks for Machine Learning Lecture 6a Overview of Mini-Batch Gradient Descent. Available online: https:\/\/www.cs.toronto.edu\/~tijmen\/csc321\/slides\/lecture_slides_lec6.pdf."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/6\/78\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:07:49Z","timestamp":1760195269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/6\/78"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,8]]},"references-count":32,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["jimaging4060078"],"URL":"https:\/\/doi.org\/10.3390\/jimaging4060078","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,8]]}}}