Skip to main content
SpringerLink
Log in
Book cover

International Conference on Ubiquitous Computing and Intelligent Information Systems

ICUIS 2021: Ubiquitous Intelligent Systems pp 147–162Cite as

A Study on Surveillance System Using Deep Learning Methods

  • Conference paper
  • First Online:

  • 383 Accesses

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 302))

Abstract

Video Surveillance data is one of the varieties of Big Data as it produces a huge amount of data required for further needs. Nowadays, the number of disruptive and aggressive activities that have been occuring are increased dramatically. Hence, securing individuals in public places like shopping malls, banks, public transportations, etc., has become significant. These public places are being equipped with CCTV (Closed Circuit Television) cameras to monitor the activity of the people. Monitoring also needs human’s consistent focus to analyze the captured scenes and to react immediately if there is any suspicious activity such as theft, sabotage, bullying, etc. But constant focus on surveillance camera videos and identifying the unusual activities in the video is a challenging task as it needs time and manpower. Hence this paper analyzes various start-of-the-art video analytics methods to detect any aggression and unusual sign.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. S. Chaudharya, M.A. Khana, C. Bhatnagar, Multiple anomalous activity detection in videos. Procedia Comput. Sci. 125, 336–345 (2018). International Conference on Smart Computing and Communications, ICSCC 2017. ScienceDirect

    Google Scholar 

  2. H. Kavalionak, C. Gennaro, G. Amato et al., Distributed video surveillance using smart cameras. J Grid Comput. 17, 59–77 (2019). https://doi.org/10.1007/s10723-018-9467-x

  3. B. Haynes, M. Daum, D. He, A. Mazumdar, M. Balazinska, A. Cheung, L. Ceze, VSS: a storage system for video analytics, in Proceedings of the 2021 International Conference on Management of Data, Association for Computing Machinery, New York, NY, USA (2021), pp. 685–696. https://doi.org/10.1145/3448016.3459242

  4. Video Surveillance Applications. https://www.videosurveillance.com/apps/

  5. Using AI to Detect Suspicious Activity in CCTV Footage. https://dzone.com/articles/using-ai-to-detect-suspicious-activity-in-cctv-foo

  6. W. Aitfares, A. Kobbane, A. Kriouile, Suspicious behavior detection of people by monitoring camera, in 2016 5th International Conference on Multimedia Computing and Systems (ICMCS) (2016)

    Google Scholar 

  7. Challenge of Video Surveillance. https://www.tfeconnect.com/the-challenges-of-video-surveillance-and-how-to-overcome-them/

  8. G. Sreenu, M.A. Saleem Durai, Intelligent video surveillance: a review through deep learning techniques for crowd analysis. J. Big Data 6(48), 1–27 (2019). https://doi.org/10.1186/s40537-019-0212-5

  9. A. Pennisi, D.D. Bloisi, L. Iocchi, Online real-time crowd behavior detection in video sequences. Comput. Vis. Image Underst. 144, 166–176 (2016).https://doi.org/10.1016/j.cviu.2015.09.010

  10. X. Wang, C.-C. Loy, Deep learning for scene-ındependent crowd analysis. Group Crowd Behav. Comput. Vis. 209–252 (2017). https://doi.org/10.1016/b978-0-12-809276-7.00012-6

  11. T. Ko, A survey on behavior analysis in video surveillance for homeland security applications, in 2008 37th IEEE Applied Imagery Pattern Recognition Workshop (2008), pp. 1–8. https://doi.org/10.1109/AIPR.2008.4906450

  12. D.R. Beddiar, B. Nini, M. Sabokrou et al., Vision-based human activity recognition: a survey. Multimedia Tools Appl. 79, 30509–30555 (2020). https://doi.org/10.1007/s11042-020-09004-3

  13. G.F. Shidik, E. Noersasongko, A. Nugraha, P.N. Andono, J. Jumanto, E.J. Kusuma, A systematic review of ıntelligence video surveillance: trends, techniques, frameworks, and datasets. IEEE Access 7, 170457–170473 (2019). https://doi.org/10.1109/ACCESS.2019.2955387

  14. T. Sikandar, K.H. Ghazali, M.F. Rabbi, ATM crime detection using image processing integrated video surveillance: a systematic review. Multimedia Syst. 25, 229–251 (2019). https://doi.org/10.1007/s00530-018-0599-4

  15. A. Senthil Murugan, K. Suganya Devi, A. Sivaranjani et al.: A study on various methods used for video summarization and moving object detection for video surveillance applications. Multimedia Tools Appl. 77, 23273–23290 (2018). https://doi.org/10.1007/s11042-018-5671-8

  16. M.M. Khin, Z.M. Win, P.P. Wai, K.T. Min, Key frame extraction techniques, in Big Data Analysis and Deep Learning Applications, ICBDL 2018. Advances in Intelligent Systems and Computing, vol. 744, ed. by T. Zin, J.W. Lin (Springer, Singapore, 2019). https://doi.org/10.1007/978-981-13-0869-7_38

  17. X. Yan, S.Z. Gilani, M. Feng, L. Zhang, H. Qin, A. Mian, Self-supervised learning to detect key frames in videos. Sensors 20(23), 6941 (2020). https://doi.org/10.3390/s20236941(2020)

    Article  Google Scholar 

  18. S. Mashtalir, O. Mikhnova, Key frame extraction from video: framework and advances. Int. J. Comput. Vis. Image Process. 4(2), 68–79 (2014). https://doi.org/10.4018/ijcvip.2014040105

  19. B.A. Athira Lekshmi, J.A. Linsely, M.P.F. Queen, P. Babu Aurtherson, Feature extraction and ımage classification using particle swarm optimization by evolving rotation-ınvariant ımage descriptors, in 2018 International Conference on Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR) (2018), pp. 1–5. https://doi.org/10.1109/ICETIETR.2018.8529083

  20. R.Vazquez-Martin, P. Nunez, A. Bandera, F. Sandoval, Spectral clustering for feature-based metric maps partitioning in a hybrid mapping framework, in 2009 IEEE International Conference on Robotics and Automation Kobe International Conference, Center Kobe, Japan, 12–17 May 2009

    Google Scholar 

  21. L. Zhou, H. Nagahashi, Real-time action recognition based on key frame detection, in Proceedings of the 9th International Conference on Machine Learning and Computing, ICMLC 2017 (2017). https://doi.org/10.1145/3055635.3056569

  22. J. Chen, K. Li, Q. Deng, K. Li, P.S. Yu, Distributed deep learning model for ıntelligent video surveillance systems with edge computing. IEEE Trans. Ind. Inf. 1–1 (2019). https://doi.org/10.1109/tii.2019.2909473

  23. P. Tong, M. Li, M. Li, J. Huang, X. Hua, Large-scale vehicle trajectory reconstruction with camera sensing network, in Proceedings of the 27th Annual International Conference on Mobile Computing and Networking (MobiCom’21), Association for Computing Machinery, New York, NY, USA (2021), pp. 188–200. https://doi.org/10.1145/3447993.3448617

  24. R. de Bem, A. Ghosh, T. Ajanthan et al., DGPose: deep generative models for human body analysis. Int. J. Comput. Vis. 128, 1537–1563 (2020). https://doi.org/10.1007/s11263-020-01306-1(2020)

    Article  Google Scholar 

  25. S. Zhang, D. Cheng, Y. Gong, D. Shi, X. Qiu, Y. Xia, Y. Zhang, Pedestrian search in surveillance videos by learning discriminative deep features. Neurocomputing 283, 120–128 (2018). https://doi.org/10.1016/j.neucom.2017.12.042

  26. H. Zhang, C. Luo, Q. Wang, M. Kitchin, A. Parmley, J. Monge-Alvarez, P. Casaseca-de-la-Higuera, A novel infrared video surveillance system using deep learning based techniques. Multimedia Tools Appl. 77(20), 26657–26676 (2018). https://doi.org/10.1007/s11042-018-5883-y

  27. H. Wang, P. Wang, X. Qian, MPNET: an end-to-end deep neural network for object detection in surveillance video. IEEE Access 6, 30296–30308 (2018)

    Article  Google Scholar 

  28. V. Singh, S. Singh, P. Gupta, Real-time anomaly recognition through CCTV using neural networks. Procedia Comput. Sci. 173, 254–263 (2020). ISSN 1877-0509. https://doi.org/10.1016/j.procs.2020.06.030

  29. Z.S. Sabri, Z. Li, Low-cost intelligent surveillance system based on fast CNN. Peer J. Comput. Sci. 7, e402 (2021). https://doi.org/10.7717/peerj-cs.402

  30. D.K. Singh, S. Paroothi, M.K. Rusia, Mohd.A. Ansari, Human crowd detection for city wide surveillance. Procedia Comput. Sci. 171, 350–359 (2020). ISSN 1877-0509. https://doi.org/10.1016/j.procs.2020.04.036

  31. M. Sivasathya, S. Mary Joans, Image feature extraction using non linear principle component analysis. Procedia Eng. 38, 911–917 (2012). ISSN 1877-7058. https://doi.org/10.1016/j.proeng.2012.06.114

  32. X. Zhang, Q. Yu, H. Yu, Physics ınspired methods for crowd video surveillance and analysis: a survey. IEEE Access 6, 66816–66830 (2018). https://doi.org/10.1109/ACCESS.2018.2878733

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

  34. D. Karthikeswaran, N. Sengottaiyan, S. Anbukaruppusamy, Video surveillance system against anti-terrorism by using adaptive linear activity classification (ALAC) technique. J. Med. Syst. 43, 256 (2019). https://doi.org/10.1007/s10916-019-1394-2(2019)

    Article  Google Scholar 

  35. W. Bouachir, R. Gouiaa, B. Li, R. Noumeir, Intelligent video surveillance for real-time detection of suicide attempts. Pattern Recognit. Lett. 110, 1–7 (2018). ISSN 0167-8655. https://doi.org/10.1016/j.patrec.2018.03.018

  36. Z. Zhao, K.M. Barijough, A. Gerstlauer, Deepthings: distributed adaptive deep learning inference on resource-constrained IoT edge clusters. IEEE Trans. Comput. Aided Design Integr. Circ. Syst. 37(1), 2348–2359 (2018)

    Google Scholar 

  37. J. Chen, K. Li, K. Bilal, X. Zhou, K. Li, P.S. Yu, A bi-layered parallel training architecture for large-scale convolutional neural networks. IEEE Trans. Parallel Distrib. Syst. 1–1 (2018)

    Google Scholar 

  38. M. Langer, A. Hall, Z. He, W. Rahayu, MPCA SGD: a method for distributed training of deep learning models on spark. IEEE Trans. Parallel Distrib. Syst. 29(11), 2540–2556 (2018)

    Article  Google Scholar 

  39. H. Kavalionak, C. Gennaro, G. Amato: Distributed video surveillance using smart cameras. J. Grid Comput. 1–19 (2018)

    Google Scholar 

  40. S. Yi, Z. Hao, Q. Zhang, Q. Zhang, W. Shi, Q. Li, Lavea: latency-aware video analytics on an edge computing platform, in Proceedings of the Second ACM/IEEE Symposium on Edge Computing (ACM, 2017), p. 15

    Google Scholar 

  41. A. Uddin, A. Alam, T. Anh, Md.S. Islam, SIAT: a distributed video analytics framework for ıntelligent video surveillance. Symmetry 11, 911 (2019). https://doi.org/10.3390/sym11070911

  42. R. Sharma, A. Sungheetha, An efficient dimension reduction based fusion of CNN and SVM model for detection of abnormal incident in video surveillance. J. Soft Comput. Paradigm (JSCP) 3(02), 55–69 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kristu Jayanti College Autonomous, Bengaluru, India

    V. Vinothina, Augustine George, G. Prathap & Jasmine Beulah

Authors
  1. V. Vinothina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Augustine George
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. Prathap
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jasmine Beulah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Vinothina .

Editor information

Editors and Affiliations

  1. Department of EEE, Shree Venkateshwara Hi-Tech Engg College, Gobichettipalayam, India

    P. Karuppusamy

  2. Castilla-La Mancha University, Ciudad Real, Spain

    Fausto Pedro García Márquez

  3. Department of Computer Science, Kennesaw State University, Marietta, GA, USA

    Tu N. Nguyen

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vinothina, V., George, A., Prathap, G., Beulah, J. (2022). A Study on Surveillance System Using Deep Learning Methods. In: Karuppusamy, P., García Márquez, F.P., Nguyen, T.N. (eds) Ubiquitous Intelligent Systems. ICUIS 2021. Smart Innovation, Systems and Technologies, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-19-2541-2_13

Download citation

Publish with us

Policies and ethics

Search

Navigation