{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:56:25Z","timestamp":1773809785205,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,6]],"date-time":"2020-12-06T00:00:00Z","timestamp":1607212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vietnam National University Ho Chi Minh City (VNU-HCM)","award":["C2019-28-04"],"award-info":[{"award-number":["C2019-28-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Decades of ongoing research have shown that background modelling is a very powerful technique, which is used in intelligent surveillance systems, in order to extract features of interest, known as foregrounds. In order to work with the dynamic nature of different scenes, many techniques of background modelling adopted the unsupervised approach of Gaussian Mixture Model with an iterative paradigm. Although the technique has had much success, a problem occurs in cases of sudden scene changes with high variation (e.g., illumination changes, camera jittering) that the model unknowingly and unnecessarily takes into account those effects and distorts the results. Therefore, this paper proposes an unsupervised, parallelized, and tensor-based approach that algorithmically works with entropy estimations. These entropy estimations are used in order to assess the uncertainty level of a constructed background, which predicts both the present and future variations from the inputs, thereby opting to use either the incoming frames to update the background or simply discard them. Our experiments suggest that this method is highly integrable into a surveillance system that consists of other functions and can be competitive with state-of-the-art methods in terms of processing speed.<\/jats:p>","DOI":"10.3390\/s20236973","type":"journal-article","created":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T21:37:42Z","timestamp":1607377062000},"page":"6973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["TensorMoG: A Tensor-Driven Gaussian Mixture Model with Dynamic Scene Adaptation for Background Modelling"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5056-8337","authenticated-orcid":false,"given":"Synh Viet-Uyen","family":"Ha","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, International University, Ho Chi Minh City 700000, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh City 700000, Vietnam"}]},{"given":"Nhat Minh","family":"Chung","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, International University, Ho Chi Minh City 700000, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh City 700000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1909-1793","authenticated-orcid":false,"given":"Hung Ngoc","family":"Phan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, International University, Ho Chi Minh City 700000, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh City 700000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9084-8977","authenticated-orcid":false,"given":"Cuong Tien","family":"Nguyen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, International University, Ho Chi Minh City 700000, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh City 700000, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MITS.2018.2806619","article-title":"Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems","volume":"10","author":"Chang","year":"2018","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1049\/iet-cvi.2018.5033","article-title":"High variation removal for background subtraction in traffic surveillance systems","volume":"12","author":"Nguyen","year":"2018","journal-title":"IET Comput. Vis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1007\/s11128-019-2458-4","article-title":"Multi-person tracking using SURF and background subtraction for surveillance","volume":"15","author":"Yu","year":"2019","journal-title":"J. Inf. Process. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-018-0131-x","article-title":"A hybrid framework combining background subtraction and deep neural networks for rapid person detection","volume":"5","author":"Kim","year":"2018","journal-title":"J. Big Data"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.4018\/IJDCF.2017070102","article-title":"An Empirical Study for Human Behavior Analysis","volume":"9","author":"Yan","year":"2017","journal-title":"Int. J. Digit. Crime Forensics"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8369","DOI":"10.1109\/ACCESS.2017.2699227","article-title":"Illumination-Invariant Background Subtraction: Comparative Review, Models, and Prospects","volume":"5","author":"Kim","year":"2017","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1177\/1748301815618302","article-title":"Image background reconstruction by Gaussian mixture based model reinforced with temporal-spatial confidence","volume":"10","author":"Chen","year":"2016","journal-title":"J. Algorithms Comput. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1109\/TPAMI.2017.2717828","article-title":"Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lim, L.A., and Keles, H.Y. (2018). Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding. arxiv.","DOI":"10.1016\/j.patrec.2018.08.002"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TCSVT.2018.2795657","article-title":"Change Detection by Training a Triplet Network for Motion Feature Extraction","volume":"29","author":"Nguyen","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"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","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_13","unstructured":"Angelov, P., and Sperduti, A. (2016, January 27\u201329). Challenges in Deep Learning. Proceedings of the ESANN, Bruges, Belgium."},{"key":"ref_14","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\u201312","author":"Bouwmans","year":"2014","journal-title":"Comput. Sci. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4768","DOI":"10.1109\/TIP.2016.2598691","article-title":"Universal Background Subtraction Using Word Consensus Models","volume":"25","author":"Bilodeau","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.trit.2016.03.005","article-title":"Background modeling methods in video analysis: A review and comparative evaluation","volume":"1","author":"Xu","year":"2016","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.cosrev.2018.01.004","article-title":"On the role and the importance of features for background modeling and foreground detection","volume":"28","author":"Bouwmans","year":"2018","journal-title":"Comput. Sci. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100204","DOI":"10.1016\/j.cosrev.2019.100204","article-title":"Background subtraction in real applications: Challenges, current models and future directions","volume":"35","author":"Bouwmans","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_19","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 (Cat. No PR00149), Fort Collins, CO, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1109\/TPAMI.2005.102","article-title":"Effective Gaussian mixture learning for video background subtraction","volume":"27","author":"Lee","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/TPAMI.2013.239","article-title":"Background Subtraction with DirichletProcess Mixture Models","volume":"36","author":"Haines","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Harville, M. (2002, January 28\u201331). A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models. Proceedings of the 7th European Conference on Computer Vision-Part III, ECCV \u201902, Copenhagen, Denmark.","DOI":"10.1007\/3-540-47977-5_36"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s10044-018-0699-y","article-title":"BMOG: Boosted Gaussian Mixture Model with Controlled Complexity for Background Subtraction","volume":"21","author":"Martins","year":"2018","journal-title":"Pattern Anal. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.sigpro.2019.02.021","article-title":"Foreground detection based on co-occurrence background model with hypothesis on degradation modification in dynamic scenes","volume":"160","author":"Zhou","year":"2019","journal-title":"Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1002\/tee.22718","article-title":"Improved background subtraction method for detecting moving objects based on GMM","volume":"13","author":"Lu","year":"2018","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, R., Bunyak, F., Seetharaman, G., and Palaniappan, K. (2014, January 23\u201328). Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.68"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4075","DOI":"10.1109\/TIP.2016.2579262","article-title":"Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements","volume":"25","author":"Cao","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.cviu.2014.01.004","article-title":"A self-adaptive Gaussian mixture model","volume":"122","author":"Chen","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1049\/iet-ipr.2017.0595","article-title":"Adaptive spatio-temporal background subtraction using improved Wronskian change detection scheme in Gaussian mixture model framework","volume":"12","author":"Panda","year":"2018","journal-title":"IET Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.neunet.2019.04.024","article-title":"Deep neural network concepts for background subtraction: A systematic review and comparative evaluation","volume":"117","author":"Bouwmans","year":"2019","journal-title":"Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Qu, Z., Yu, S., and Fu, M. (2016, January 19\u201321). Motion background modeling based on context-encoder. Proceedings of the 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR), Lodz, Poland.","DOI":"10.1109\/ICAIPR.2016.7585207"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xu, L., Li, Y., Wang, Y., and Chen, E. (2015, January 25\u201330). Temporally Adaptive Restricted Boltzmann Machine for Background Modeling. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence AAAI\u201915, Austin, TX, USA.","DOI":"10.1609\/aaai.v29i1.9481"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tao, Y., Palasek, P., Ling, Z., and Patras, I. (September, January 29). Background modelling based on generative unet. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078483"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liang, D., Pan, J., Sun, H., and Zhou, H. (2019). Spatio-Temporal Attention Model for Foreground Detection in Cross-Scene Surveillance Videos. Sensors, 19.","DOI":"10.3390\/s19235142"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1007\/s10044-019-00845-9","article-title":"Learning multi-scale features for foreground segmentation","volume":"23","author":"Lim","year":"2019","journal-title":"Pattern Anal. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"16010","DOI":"10.1109\/ACCESS.2018.2817129","article-title":"Background Subtraction Using Multiscale Fully Convolutional Network","volume":"6","author":"Zeng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z. (2004, January 23\u201326). Improved adaptive Gaussian mixture model for background subtraction. Proceedings of the 17th International Conference on Pattern Recognition ICPR 2004, Cambridge UK.","DOI":"10.1109\/ICPR.2004.1333992"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"St-Charles, P., and Bilodeau, G. (2014, January 24\u201326). Improving background subtraction using Local Binary Similarity Patterns. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA.","DOI":"10.1109\/WACV.2014.6836059"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/TIP.2014.2378053","article-title":"SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity","volume":"24","author":"Bilodeau","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_41","unstructured":"Noh, S., and Jeon, M. (2012, January 5\u20139). A New Framework for Background Subtraction Using Multiple Cues. Proceedings of the 11th Asian Conference on Computer Vision\u2014Volume Part III, ACCV\u201912, Daejeon, Korea."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bloisi, D., and Iocchi, L. (2012, January 5\u20137). Independent Multimodal Background Subtraction. Proceedings of the CompIMAGE 2012, Rome, Italy.","DOI":"10.1201\/b12753-8"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JEI.27.2.023002","article-title":"SWCD: A sliding window and self-regulated learning-based background updating method for change detection in videos","volume":"27","author":"Isik","year":"2018","journal-title":"J. Electron. Imaging"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hofmann, M., Tiefenbacher, P., and Rigoll, G. (2012, January 16\u201321). Background segmentation with feedback: The Pixel-Based Adaptive Segmenter. Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6238925"},{"key":"ref_45","unstructured":"Farnoosh, A., Rezaei, B., and Ostadabbas, S. (2019). DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences. arxiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"59143","DOI":"10.1109\/ACCESS.2019.2914961","article-title":"A Comprehensive Survey of Video Datasets for Background Subtraction","volume":"7","author":"Kalsotra","year":"2019","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jodoin, P., 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, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.126"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, S., Florencio, D., Zhao, Y., Cook, C., and Li, W. (2017, January 17\u201320). Foreground detection in camouflaged scenes. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297083"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5244","DOI":"10.1109\/TIP.2017.2728181","article-title":"Extensive Benchmark and Survey of Modeling Methods for Scene Background Initialization","volume":"26","author":"Jodoin","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Murino, V., Puppo, E., Sona, D., Cristani, M., and Sansone, C. (2015). Towards Benchmarking Scene Background Initialization. New Trends in Image Analysis and Processing\u2014ICIAP 2015 Workshops, Springer International Publishing.","DOI":"10.1007\/978-3-319-23222-5"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6973\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:41:39Z","timestamp":1760179299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,6]]},"references-count":51,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20236973"],"URL":"https:\/\/doi.org\/10.3390\/s20236973","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,6]]}}}