Firearm Detection from Surveillance Cameras Using Image Processing and Machine Learning Techniques

  • Fraol GelanaEmail author
  • Arvind Yadav
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 851)


The increasing number of terrorist acts and lone wolf attacks on places of public gathering such as Hotels and Cinemas has solidified the need for much denser Closed-circuit Television (CCTV) systems. The increasing number of CCTV cameras has deemed it almost impossible for a human operator to inspect all the video streams and detect possible terror events. One of the common types of terror event is called “Active Shooter”. Events such as the 2008 Mumbai shooting, shooting at the movie theater in Colorado (USA), Oslo (Norway) and recently an attacker opened gun fire at an outdoor music festival in Las Vegas on Oct. 1, 2017, USA. Therefore in this work, the detection of an “Active Shooter” carrying a non-concealed firearm and alerting the CCTV operator of a potentially dangerous event both visually and audibly has been carried out. The proposed approach of gun detection uses a feature extraction techniques and a convolutional neural network classifier for classifying objects as either a gun or not a gun. And the classification accuracy achieved by the proposed approach is 97.78%.


CCTV Neural network Firearm detection Background subtraction 


  1. 1.
    Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. Paper presented. In: Proceedings of the IEEE 17th International Conference on Pattern Recognition, Cambridge, UK, vol. 2, pp. 28–31 (2004)Google Scholar
  2. 2.
    Olivier, B., Droogenbroeck, M.V.: Vibe: a universal background subtraction algorithm for video sequences. In: IEEE Transactions on Image processing, vol. 20, no. 6, pp. 1709–1724 (2011)Google Scholar
  3. 3.
    Sieradzki, R., Grega, M., Lach, S.: Automated recognition of firearms in surveillance video. In: IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), San Diego, CA, USA, pp. 45–50 (2013)Google Scholar
  4. 4.
    Megherbi, N., Flitton, G.T., Breckon, T.P.: A classifier based approach for the detection of potential threats in ct based baggage screening. In: 17th IEEE International Conference on Image Processing (ICIP), Hong Kong, China, pp. 1833–1836 (2010)Google Scholar
  5. 5.
    Akcay, C., Kundergorski, M.E., Devereux, M., Breckon, T.P.: Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery. In: IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, pp. 1057–1061 (2016)Google Scholar
  6. 6.
    Merry, D., Mondragon, G., Riffo, V., Zuccar, I.: Detection of regular objects in baggage using multiple X-ray views. Insight-Non-Destr. Test. Cond. Monit. 55(1), 16–20 (2013)CrossRefGoogle Scholar
  7. 7.
    Roomi, M., Rajashankarii, R.: Detection of concealed weapons in X-ray images using fuzzy k-nn. Int. J. Comput. Sci. Eng. Inf. Technol. 2(2), 65–70 (2012)Google Scholar
  8. 8.
    Lai, J., Maples, S.: Developing a real-time gun detection classifier. Accessed on August 2017 (2017).
  9. 9.
    O’Reilly, D., Bowring, N., Harmer, S.: Signal processing techniques for concealed weapon detection by use of neural networks. In: IEEE 27th Convention of Electrical & Electronics Engineers in Israel (IEEEI), Eilat, Israel, pp. 1–4 (2012)Google Scholar
  10. 10.
    Olsmos, R., Tabik, S., Herrera, F.: Automatic handgun detection alarm in videos using deep learning. Neurocomputing 275, 66–72 (2018)CrossRefGoogle Scholar
  11. 11.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)CrossRefGoogle Scholar
  12. 12.
    Brutzer, S., Benjamin, H., Gunther, H.: Evaluation of background subtraction techniques for video surveillance. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CP, USA, pp. 1937–1944, 20th–25th June 2011Google Scholar
  13. 13.
    Gun Video Database. Accessed Sept 2017.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringParul Institute of Engineering and TechnologyVadodaraIndia

Personalised recommendations