Skip to main content
SpringerLink
Log in

Python and OpenCV in Automation of Live Surveillance

  • Conference paper
  • First Online:
Machine Learning and Information Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1101))

Abstract

There have been many uses of image recognition and feature detection in recent days. With the growth in the popularity of Python and simplification of automation allows us to bring the live surveillance in the domain. Nowadays, the situation, with the sectors like banking, needs high amount of security due to their increasing importance. Especially on remote location where services like ATM are provided, the security factor becomes main concern. By automation of surveillance, there will be an efficient way to reduce stress on security.

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

Access this chapter

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

References

  1. Viola, Paul, and Michael Jones. 2001. Rapid object detection using a boosted cascade of simple features. CVPR (1): 511–518.

    Google Scholar 

  2. Amit, Yali, Donald German, and Kenneth Wilder. 1997. Joint induction of shape features and tree classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 11: 1300–1305.

    Article  Google Scholar 

  3. Rezaei, Mahdi. 2013. Creating a cascade of haar-like classifiers: Step by step. Aplikasi Pendeteksian Ras kucing dengan mendeteksi wajah kucing dengan metode viola jones.

    Google Scholar 

  4. Mahdi, Rezaei. Creating a cascade of Harr like classifiers. Department of computer science, Auckland.

    Google Scholar 

  5. Sung, Kah K., and Tomaso Poggio. 1994. Example based learning for view-based human face detection. Massachusetts Institute of Technology Cambridge Artificial Intelligence Lab, No. AI-M-1521.

    Google Scholar 

  6. ElSaid, W.K. A System for managing attendance of academic staff members in university development programs using face recognition.

    Google Scholar 

  7. Greenspan, Hayit et al. 1994. Overcomplete steerable pyramid filters and rotation invariance, 222–228.

    Google Scholar 

  8. Schneiderman, Henry, Takeo Kanade. 2000. A statistical approach to 3D object detection applied to faces and cars. Carnegie Mellon University, the Robotics Institute.

    Google Scholar 

  9. Tsotsos, John K., et al. 1995. Modeling visual attention via selective tuning. Artificial Intelligence 78 (1-2): 507–545.

    Article  MathSciNet  Google Scholar 

  10. Itti, Laurent, Christof Koch, and Ernst Niebur. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 11: 1254–1259.

    Article  Google Scholar 

  11. Fleuret, Francois, and Donald Geman. 2001. Coarse-to-fine face detection. International Journal of computer vision. 41 (1–2): 85–107.

    Google Scholar 

  12. Schapire, Robert E., et al. 1998. Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics 26 (5): 1651–1686.

    Article  MathSciNet  Google Scholar 

  13. Osuna, Edgar, Robert Freund, and Federico Girosi. 1997. Training support vector machines: an application to face detection. cvpr. 97: 130–136.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Vishwakarma Institute of Technology, 666, Upper Indra Nagar, Bibwewadi, Pune, 411037, India

    Nilesh Navghare, Pratik Kedar, Prasad Sangamkar & Manas Mahajan

Authors
  1. Nilesh Navghare
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pratik Kedar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prasad Sangamkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manas Mahajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nilesh Navghare .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Debabala Swain

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

  3. Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Pradeep K. Gupta

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Navghare, N., Kedar, P., Sangamkar, P., Mahajan, M. (2020). Python and OpenCV in Automation of Live Surveillance. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_22

Download citation

Publish with us

Policies and ethics

Search

Navigation