Python and OpenCV in Automation of Live Surveillance

  • Nilesh NavghareEmail author
  • Pratik Kedar
  • Prasad Sangamkar
  • Manas Mahajan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1101)


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.


Image and video processing Haar Cascades [1] Haar-like feature [1] Integral images OpenCV–Python 


  1. 1.
    Viola, Paul, and Michael Jones. 2001. Rapid object detection using a boosted cascade of simple features. CVPR (1): 511–518.Google Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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. 4.
    Mahdi, Rezaei. Creating a cascade of Harr like classifiers. Department of computer science, Auckland.Google Scholar
  5. 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. 6.
    ElSaid, W.K. A System for managing attendance of academic staff members in university development programs using face recognition.Google Scholar
  7. 7.
    Greenspan, Hayit et al. 1994. Overcomplete steerable pyramid filters and rotation invariance, 222–228.Google Scholar
  8. 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. 9.
    Tsotsos, John K., et al. 1995. Modeling visual attention via selective tuning. Artificial Intelligence 78 (1-2): 507–545.MathSciNetCrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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. 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.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Osuna, Edgar, Robert Freund, and Federico Girosi. 1997. Training support vector machines: an application to face detection. cvpr. 97: 130–136.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Nilesh Navghare
    • 1
    Email author
  • Pratik Kedar
    • 1
  • Prasad Sangamkar
    • 1
  • Manas Mahajan
    • 1
  1. 1.Computer Engineering DepartmentVishwakarma Institute of TechnologyPuneIndia

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