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Design of an intelligent video surveillance system for crime prevention: applying deep learning technology

  • 1135T: Social Multimedia Processing
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A Correction to this article was published on 22 April 2021

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Abstract

As the security threat and crime rate have been increased all over the globe, the video surveillance system using closed-circuit television (CCTV) has become an essential tool for many security-related applications and is widely used in many areas as a monitoring system. However, most of the data collected by the video surveillance system is used as evidence of objective data after crime and disaster have occurred. And, often time, video surveillance systems tend to be used in a passive manner due to the high cost and human resources. The video surveillance system should actively respond to detect crime and accidents in advance through real-time monitoring and immediately transmit data in case of an accident. This study proposes developing an intelligent video surveillance system that can actively monitor in real-time without human input. In solving the problems of the existing video surveillance system, deep learning technology will be carried through the data processing model design to visualize data for crime detection after building an artificial intelligence server and video surveillance camera. In addition, this design proposes an intelligent surveillance system to quickly and effectively detect crimes by sending a video image and notification message to the web through real-time processing.

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References

  1. Ahn H, Kim D, Kim YS (2015) Generating new ground truth data by editing previous data from integrated video annotation database. BigDAS ‘15: Proceedings of the 2015 International Conference on Big Data Applications and Services, pp 208–212

  2. 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

  3. 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 511–521

  4. Bengio Y, LeCun Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  5. CCTV Cameras explained, tech cube: security sales success. https://www.techcube.co.uk/blog/cctv-cameras-explained/. Accessed 10 Jan 2020

  6. 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, Liu X, Shah H (2016) Wide & deep learning for recommender systems. Cornell University, arXiv:1606.07792v1, June 24

  7. Chollet F (2018) Deep learning with python. Mannng Publications Co., Shelter Island

    Google Scholar 

  8. Gibert X, Patel VM, Chellappa R (2015) Deep multitask learning for railway track inspection. IEEE Trans Intell Transp Syst 18:153–164

    Article  Google Scholar 

  9. 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

  10. 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]

  11. 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

  12. 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 193–198

  13. Ju YW, Yi SJ (2013) Implementing database methods for increasing the performance of intelligent CCTV. Int J Secur Appl 7:113–120

    Google Scholar 

  14. Kumar V, Svensson J (2015) Promoting social change and democracy through information technology, IGI Global, Hershey, pp 75. ISBN 9781466685031

  15. Lei J, Zhang B, Ling H (2019) Deep learning face representation by fixed erasing in facial landmarks. Multimed Tools Appl 78:27703–27718

    Article  Google Scholar 

  16. 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 38:1254–1269

    Article  Google Scholar 

  17. Little DD, Ross SE (2013) APMs and airport mobility – 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

  18. Liu Y, Zhang D, Lu G (2008) Region-based image retrieval with high-level semantics using decision tree learning. Pattern Recogn 41(8):2554–2570

    Article  Google Scholar 

  19. 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

  20. Mationlansk A, Maksimova A, Dziech A (2016) CCTV object detection with fuzzy classification and image enhancement. Multimed Tools Appl 75:10513–10528

    Article  Google Scholar 

  21. 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

    Google Scholar 

  22. 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 3:338–358

    Google Scholar 

  23. Muhammad K, Ahmad J, Baik SW (2018) Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288:30–42

    Article  Google Scholar 

  24. 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 2020

  25. 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):2107–2119

    Article  Google Scholar 

  26. 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

  27. 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, 10–14 June 2019, Enschede, The Netherlands

  28. 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. 4–7

  29. 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):191–204

    Article  Google Scholar 

  30. 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

    Article  Google Scholar 

  31. 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

  32. 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 169–184

  33. 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 2016–13th Annual Body Sensor Networks Conference

  34. 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 6:1–14, arXiv:1506.01497v3

  35. 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

  36. 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):1–16

    Google Scholar 

  37. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  38. 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):939–942

    Google Scholar 

  39. Sreenu G, Saleem Durai MA (2019) Intelligent video surveillance: a review through deep learning techniques for crowd analysis. Journal of Big Data 6:1–27

    Article  Google Scholar 

  40. Sur C (2019) Survey of deep learning and architectures for visual captioning – transitioning between media and natural languages. Multimed Tools Appl 78:32187–32237

    Article  Google Scholar 

  41. Turchini F, Seidenari L, Uricchio T, Bimbo AD (2018) Deep learning based surveillance system for open critical areas. Inventions 3(69):1–13

    Google Scholar 

  42. 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

  43. Vishnu VCM, Rajalakshmi M, Nedunchezhian R (2018) Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control. Cluster Comput 21:135–147. https://doi.org/10.1007/s10586-017-0974-5

  44. 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 116:194–209

    Article  Google Scholar 

  45. 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):148–157

    Article  Google Scholar 

  46. Zablocki M, Gosciewska K, Frejlichowski D, Hofman R (2014) Intelligent video surveillance systmes for public spaces – a survey. J Theor Appl Comput Sci 8:13–27

    Google Scholar 

  47. Zhang P, Thomas T, Emmanuel S (2012) Privacy enabled video surveillance using a two state Markov tracking algorithm. Multimed Syst 18(2):175–199

    Article  Google Scholar 

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Acknowledgements

This work is based on the design constructed by Mr. Minsu Kim, the CEO of CLOMOUNT Co., Ltd. The authors would like to thank Mr. Kim for providing data sources to develop this research.

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Authors and Affiliations

  1. Department of Technology Entrepreneurship, Dongguk University-Seoul, Seoul, 04620, Korea

    Chang-Soo Sung

  2. Discipline of Information Technology, Media and Communication, Murdoch University, Murdoch, Western Australia, 6150, Australia

    Joo Yeon Park

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  1. Chang-Soo Sung
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Correspondence to Joo Yeon Park.

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Sung, CS., Park, J.Y. Design of an intelligent video surveillance system for crime prevention: applying deep learning technology. Multimed Tools Appl 80, 34297–34309 (2021). https://doi.org/10.1007/s11042-021-10809-z

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