{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T10:24:54Z","timestamp":1775039094344,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"26-27","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s11042-021-10809-z","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T16:04:11Z","timestamp":1616601851000},"page":"34297-34309","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Design of an intelligent video surveillance system for crime prevention: applying deep learning technology"],"prefix":"10.1007","volume":"80","author":[{"given":"Chang-Soo","family":"Sung","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5231-5405","authenticated-orcid":false,"given":"Joo Yeon","family":"Park","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"key":"10809_CR1","doi-asserted-by":"crossref","unstructured":"Ahn H, Kim D, Kim YS (2015) Generating new ground truth data by editing previous data from integrated video annotation database. BigDAS \u201815: Proceedings of the 2015 International Conference on Big Data Applications and Services, pp 208\u2013212","DOI":"10.1145\/2837060.2837097"},{"key":"10809_CR2","unstructured":"Allied, Telesis, Intelligent video surveillance: Recent trends and what lies ahead. https:\/\/www.alliedtelesis.com\/blog\/intelligent-video-surveillance-recent-trends-and-what-lies-ahead. Accessed 10 Jan 2020"},{"key":"10809_CR3","doi-asserted-by":"crossref","unstructured":"Barhm M, Qwasmi N, Qureshi F, el Khatib K (2011) Negotiating privacy preferences in video surveillance systems. In: Mehrotra WK, Mohan WK, Oh J, Varshney P, Ali M (eds) Modern approaches in applied intelligence, vol 6704. Springer, Berlin Heidelberg, pp 511\u2013521","DOI":"10.1007\/978-3-642-21827-9_52"},{"issue":"7553","key":"10809_CR4","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Bengio","year":"2015","unstructured":"Bengio Y, LeCun Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"10809_CR5","unstructured":"CCTV Cameras explained, tech cube: security sales success. https:\/\/www.techcube.co.uk\/blog\/cctv-cameras-explained\/. Accessed 10 Jan 2020"},{"key":"10809_CR6","doi-asserted-by":"crossref","unstructured":"Cheng HT, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado GS, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V,\u00a0Liu X, Shah H (2016) Wide & deep learning for recommender systems. Cornell University, arXiv:1606.07792v1, June 24","DOI":"10.1145\/2988450.2988454"},{"key":"10809_CR7","volume-title":"Deep learning with python","author":"F Chollet","year":"2018","unstructured":"Chollet F (2018) Deep learning with python. Mannng Publications Co., Shelter Island"},{"key":"10809_CR8","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TITS.2016.2568758","volume":"18","author":"X Gibert","year":"2015","unstructured":"Gibert X, Patel VM, Chellappa R (2015) Deep multitask learning for railway track inspection. IEEE Trans Intell Transp Syst 18:153\u2013164","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10809_CR9","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2013) Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR 2017: computer vision for microscopy imae analysis (CVMI) workshop, Cornell University, arXiv:1311.2524v5","DOI":"10.1109\/CVPR.2014.81"},{"key":"10809_CR10","unstructured":"Guo Y, Wang H, Hu Q, Liu H, Liu L, Bennamoun M (2019) Deep learning for 3D point clouds: a survey. arXiv: 1912, 12033v1[cs.CV]"},{"key":"10809_CR11","unstructured":"Hung J, Ravel D, Lopes SCP, Rangel G, Nery OA, Malleret B, Nosten F, Lacerda MG, Ferreira MU, Renia L, Duraisingh MT, Costa FTM, Marti M, Carpenter AE (2017) Applying faster R-CNN for oject detection on malaria images, CVPR 2017: computer vision for microscopy imae analysis (CVMI) workshop, Cornell University, arXiv:1804.09548v2"},{"key":"10809_CR12","doi-asserted-by":"crossref","unstructured":"Jain H, Vikram A, Mohana, Kashyap A, Jain A (2020) Weapon detection using artificial intelligence and deep learning for security applications, Proceedings of the International Conference on Electronics and Sustainable Communication Systems (ICESC 2020), IEEE Xplore Part Number: CFP20V66-ART, ISBN: 978-1-7281-4108-4, pp 193\u2013198","DOI":"10.1109\/ICESC48915.2020.9155832"},{"key":"10809_CR13","first-page":"113","volume":"7","author":"YW Ju","year":"2013","unstructured":"Ju YW, Yi SJ (2013) Implementing database methods for increasing the performance of intelligent CCTV. Int J Secur Appl 7:113\u2013120","journal-title":"Int J Secur Appl"},{"key":"10809_CR14","doi-asserted-by":"crossref","unstructured":"Kumar V, Svensson J (2015) Promoting social change and democracy through information technology, IGI Global, Hershey, pp 75. ISBN 9781466685031","DOI":"10.4018\/978-1-4666-8502-4"},{"key":"10809_CR15","doi-asserted-by":"publisher","first-page":"27703","DOI":"10.1007\/s11042-019-07892-8","volume":"78","author":"J Lei","year":"2019","unstructured":"Lei J, Zhang B, Ling H (2019) Deep learning face representation by fixed erasing in facial landmarks. Multimed Tools Appl 78:27703\u201327718","journal-title":"Multimed Tools Appl"},{"key":"10809_CR16","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/TSMCB.2008.927265","volume":"38","author":"L Li","year":"2008","unstructured":"Li L, Huang W, Gu IYH, Luo R, Tian Q (2008) An efficient sequential approach to tracking multiple object through crowds for real-time intelligent CCTV systems. IEEE Trans Syst Man Cybern 38:1254\u20131269","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"10809_CR17","doi-asserted-by":"crossref","unstructured":"Little DD, Ross SE (2013) APMs and airport mobility \u2013 Historic trends and future possibilities. Automated people movers and transit systems (ed.): Computer Science Today. Recent Trends and Developments. Sproute, American Society of Civil Engineers, Reston","DOI":"10.1061\/9780784412862.001"},{"issue":"8","key":"10809_CR18","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1016\/j.patcog.2007.12.003","volume":"41","author":"Y Liu","year":"2008","unstructured":"Liu Y, Zhang D, Lu G (2008) Region-based image retrieval with high-level semantics using decision tree learning. Pattern Recogn 41(8):2554\u20132570","journal-title":"Pattern Recogn"},{"key":"10809_CR19","unstructured":"Mathworks (2020) Deep Learning, What is deep learning?_3 things you need to know. https:\/\/www.mathworks.com\/discovery\/deep-learning.html. Accessed 28 Apr 2020"},{"key":"10809_CR20","doi-asserted-by":"publisher","first-page":"10513","DOI":"10.1007\/s11042-015-2697-z","volume":"75","author":"A Mationlansk","year":"2016","unstructured":"Mationlansk A, Maksimova A, Dziech A (2016) CCTV object detection with fuzzy classification and image enhancement. Multimed Tools Appl 75:10513\u201310528","journal-title":"Multimed Tools Appl"},{"key":"10809_CR21","volume-title":"Deep Learning Techniques for Biomedical and Health Informatics. Studies in Big Data","author":"S Mittal","year":"2019","unstructured":"Mittal S, Hasija Y (2019) Applications of deep learning in healthcare and biomedicine. In: Dash S, Acharya B, Mittal M, Abraham A, Kelemen A (eds) Deep Learning Techniques for Biomedical and Health Informatics. Studies in Big Data, vol 68. Springer, Cham"},{"key":"10809_CR22","first-page":"338","volume":"3","author":"K Morioka","year":"2010","unstructured":"Morioka K, Kovacs S, Lee JH, Korondi P (2010) A cooperative object tracking system with fuzzy-based adaptive camera selection. Int J Smart Sens Intell Syst 3:338\u2013358","journal-title":"Int J Smart Sens Intell Syst"},{"key":"10809_CR23","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.neucom.2017.04.083","volume":"288","author":"K Muhammad","year":"2018","unstructured":"Muhammad K, Ahmad J, Baik SW (2018) Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288:30\u201342","journal-title":"Neurocomputing"},{"key":"10809_CR24","unstructured":"Mwiti D (2019) A 2019 guide to object detection, Heartbeat, July 18, 2019. https:\/\/heartbeat.fritz.ai\/a-2019-guide-to-object-detection-9509987954c3. Accessed 3 Mar\u00a02020"},{"issue":"8","key":"10809_CR25","doi-asserted-by":"publisher","first-page":"2107","DOI":"10.1109\/TKDE.2015.2399298","volume":"27","author":"L Nie","year":"2015","unstructured":"Nie L, Wang M, Zhang L, Yan S, Zhang B, Chua TS (2015) Disease inference from health-related questions via sparse deep learning. IEEE Trans Knowl Data Eng 27(8):2107\u20132119","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10809_CR26","unstructured":"Nielsen A (2017) Video surveillance threatens privacy, experts say. The daily universe. June 28. https:\/\/universe.byu.edu\/2017\/06\/28\/video-surveillance-threatens-privacy. Accessed 28 Apr 2020"},{"key":"10809_CR27","doi-asserted-by":"crossref","unstructured":"Niu L. Song YQ (2019) A faster R-CNN approach for extracting indoor navigation graph from building designs. The international archives of the photogrammetry, remote sensing and spatial information sciences, vol XLII-2\/W13, ISPRS Geospatial Week 2019, 10\u201314 June 2019, Enschede, The Netherlands","DOI":"10.5194\/isprs-archives-XLII-2-W13-865-2019"},{"key":"10809_CR28","doi-asserted-by":"crossref","unstructured":"Ogle RI, Rho JC, Clarke RJ (2018) Artificial intelligence in disaster risk communication: A systematic literature review. 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM). Dec. 4\u20137","DOI":"10.1109\/ICT-DM.2018.8636380"},{"issue":"2","key":"10809_CR29","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1504\/IJSHC.2019.101602","volume":"3","author":"N Paliwal","year":"2019","unstructured":"Paliwal N, Vanjani P, Liu JW, Saini S, Sharma A (2019) Image processing-based intelligent robotic system for assistance of agricultural crops. Int J Soc Humanist Comput (IJSHC) 3(2):191\u2013204","journal-title":"Int J Soc Humanist Comput (IJSHC)"},{"issue":"9","key":"10809_CR30","doi-asserted-by":"publisher","first-page":"959","DOI":"10.3390\/electronics8090959","volume":"8","author":"L Qi","year":"2019","unstructured":"Qi L, Li B, Chen L, Wang W, Dong L, Jia X, Huang J, Ge C, Xue G, Wang D (2019) Ship target detection algorithm based on improved faster R-CNN. Electronics 8(9):959","journal-title":"Electronics"},{"key":"10809_CR31","doi-asserted-by":"publisher","unstructured":"Rai M, Asim A, Husain TM, Yadav RK (2018) Advanced intelligent video surveillance system (AIVSS): a future aspect. In: Neves AJR (ed) Intelligent video surveillance. IntechOpen, November 5. https:\/\/doi.org\/10.5772\/intechopen.76444","DOI":"10.5772\/intechopen.76444"},{"key":"10809_CR32","doi-asserted-by":"crossref","unstructured":"Rajpoot QM, Jensen CD (2014) Security and privacy in video surveillance: Requirements and challenges. In: Cuppens-Boulahia N, Cuppens F, Jajodia S, El Kalam A, Sans AT (eds) ICT Systems Security and Privacy Protection. Sections 2014, 428. IFIP Advances in Information and Communication Technology, Springer, Berlin, pp 169\u2013184","DOI":"10.1007\/978-3-642-55415-5_14"},{"key":"10809_CR33","doi-asserted-by":"crossref","unstructured":"Ravi D, Wong C, Lo B, Yang GZ (2016) Deep learning for human activity recognition: a resource efficient implementation on low-power devices. In: BSN 2016\u201313th Annual Body Sensor Networks Conference","DOI":"10.1109\/BSN.2016.7516235"},{"key":"10809_CR34","doi-asserted-by":"crossref","unstructured":"Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems. Jan 6:1\u201314, arXiv:1506.01497v3","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"10809_CR35","doi-asserted-by":"crossref","unstructured":"Salahat E, Saleh H, Mohammad B, Al-Qutayri M, Sluzek A, Ismail M (2013) Automated real-time video surveillance algorithms for SoC implementation: A survey. IEEE International Conference on Electronics Circuits and Systems. December 2013","DOI":"10.1109\/ICECS.2013.6815354"},{"issue":"973","key":"10809_CR36","first-page":"1","volume":"11","author":"S Saravi","year":"2019","unstructured":"Saravi S, Kalawsky R, Joannou D, Casado MR. Fu G, Meng F (2019) Use of artificial intelligence to improve resilience and preparedness against adverse flood events. Water 11(973):1\u201316","journal-title":"Water"},{"key":"10809_CR37","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"issue":"1C2","key":"10809_CR38","first-page":"939","volume":"8","author":"V Singh","year":"2019","unstructured":"Singh V, Gupta R (2019) Novel framework of semantic based image reterival by convoluted features with nonlinear mapping in cyberspace international. J Recent Technol Eng (IJRTE) 8(1C2):939\u2013942","journal-title":"J Recent Technol Eng (IJRTE)"},{"key":"10809_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0212-5","volume":"6","author":"G Sreenu","year":"2019","unstructured":"Sreenu G, Saleem Durai MA (2019) Intelligent video surveillance: a review through deep learning techniques for crowd analysis. Journal of Big Data 6:1\u201327","journal-title":"Journal of Big Data"},{"key":"10809_CR40","doi-asserted-by":"publisher","first-page":"32187","DOI":"10.1007\/s11042-019-08021-1","volume":"78","author":"C Sur","year":"2019","unstructured":"Sur C (2019) Survey of deep learning and architectures for visual captioning \u2013 transitioning between media and natural languages. Multimed Tools Appl 78:32187\u201332237","journal-title":"Multimed Tools Appl"},{"issue":"69","key":"10809_CR41","first-page":"1","volume":"3","author":"F Turchini","year":"2018","unstructured":"Turchini F, Seidenari L, Uricchio T, Bimbo AD (2018) Deep learning based surveillance system for open critical areas. Inventions 3(69):1\u201313","journal-title":"Inventions"},{"key":"10809_CR42","unstructured":"Vincent J (2018) Artificial intelligence is going to supercharge surveillance, What happens when digital eyes get the brains to match? Jan. 23, 2018, The Verge. https:\/\/www.theverge.com\/2018\/1\/23\/16907238\/artificial-intelligence-surveillance-cameras-security. Accessed 13 Jan 2020"},{"key":"10809_CR43","doi-asserted-by":"publisher","unstructured":"Vishnu VCM, Rajalakshmi M, Nedunchezhian R (2018) Intelligent traffic video surveillance and accident detection system with dynamic traffic signal\u00a0control. Cluster Comput 21:135\u2013147. https:\/\/doi.org\/10.1007\/s10586-017-0974-5","DOI":"10.1007\/s10586-017-0974-5"},{"key":"10809_CR44","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.cviu.2011.10.001","volume":"116","author":"A Williem","year":"2012","unstructured":"Williem A, Madasu V, Boles W, Yarlagadda P (2012) A suspicious behavior detection using a context space model for smart surveillance systems. Comput Vis Image Underst 116:194\u2013209","journal-title":"Comput Vis Image Underst"},{"issue":"2","key":"10809_CR45","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1504\/IJSHC.2019.101598","volume":"3","author":"HM Yang","year":"2019","unstructured":"Yang HM, Lim DW, Choi YS, Kang JG, Kim IH, Lin A, Jung JW (2019) Image-based human sperm counting method. Int J Soc Humanist Comput (IJSHC) 3(2):148\u2013157","journal-title":"Int J Soc Humanist Comput (IJSHC)"},{"key":"10809_CR46","first-page":"13","volume":"8","author":"M Zablocki","year":"2014","unstructured":"Zablocki M, Gosciewska K, Frejlichowski D, Hofman R (2014) Intelligent video surveillance systmes for public spaces \u2013 a survey. J Theor Appl Comput Sci 8:13\u201327","journal-title":"J Theor Appl Comput Sci"},{"issue":"2","key":"10809_CR47","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00530-011-0247-8","volume":"18","author":"P Zhang","year":"2012","unstructured":"Zhang P, Thomas T, Emmanuel S (2012) Privacy enabled video surveillance using a two state Markov tracking algorithm. Multimed Syst 18(2):175\u2013199","journal-title":"Multimed Syst"}],"updated-by":[{"DOI":"10.1007\/s11042-021-10931-y","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000}}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10809-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10809-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10809-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T18:12:18Z","timestamp":1636740738000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10809-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,24]]},"references-count":47,"journal-issue":{"issue":"26-27","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["10809"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10809-z","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s11042-021-10931-y","asserted-by":"object"}]},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,24]]},"assertion":[{"value":"17 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2021","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11042-021-10931-y","URL":"https:\/\/doi.org\/10.1007\/s11042-021-10931-y","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}