Skip to main content

Weapon Detection Using PTZ Cameras

  • Conference paper
  • First Online:
Robotics, Computer Vision and Intelligent Systems (ROBOVIS 2024)

Abstract

Massive shooting in public places are a stigma in some countries. Computer vision techniques are being actively researched in the last few years to process video from surveillance cameras and immediately detect the presence of an armed individual. The research, however, has focused on images taken from cameras that are (as is the typical case) far from the entrance where the individual first appears. However, most modern video surveillance cameras have some pan-tilt-zoom (PTZ) capabilities, fully controllable by the operator or some control software. In this paper, we make the first (as far as the authors know) exploration on the use of PTZ cameras in this particular problem. Our results unequivocally reveal the transformative impact of integrating PTZ functionality, particularly zoom and tracking capabilities, on the overall performance of these weapon detection models. Experiments were carefully executed in controlled environments, including laboratory and classroom settings, allowing for a comprehensive evaluation. In these settings, the utility of PTZ in improving detection outcomes became evident, especially when confronted with challenging conditions such as dim lighting or multiple individuals in the scene. This research underscores the immense potential of modern PTZ cameras for automatic firearm detection. This advancement holds the promise of augmenting public safety and security.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bhatti, M.T., Khan, M.G., Aslam, M., Fiaz, M.J.: Weapon detection in real-time CCTV videos using deep learning. IEEE Access 9, 34366–34382 (2021)

    Article  Google Scholar 

  2. Chatterjee, R., Chatterjee, A.: Pose4gun: a pose-based machine learning approach to detect small firearms from visual media. Multimed. Tools Appl. 1–27 (2023)

    Google Scholar 

  3. David, P., Harrison, A., Sreenivas, R., Whitman, J.: Context-Aware Visual Search Using a Pan-Tilt-Zoom Camera. Army Research Laboratory, Aberdeen Proving Ground, MD (2023)

    Google Scholar 

  4. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  5. Ferone, A., Maddalena, L., Petrosino, A.: Neural moving object detection by pan-tilt-zoom cameras. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds.) Neural Nets and Surroundings. SIST, vol. 19, pp. 129–138. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35467-0_14

    Chapter  Google Scholar 

  6. Goyal, A., et al.: Automatic border surveillance using machine learning in remote video surveillance systems. In: Hitendra Sarma, T., Sankar, V., Shaik, R. (eds.) Emerging Trends in Electrical, Communications, and Information Technologies. LNEE, vol. 569, pp. 751–760. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-8942-9_64

    Chapter  Google Scholar 

  7. Hernández, E.: Tiroteo en Nashville: cronología de la tragedia en The Covenant School. La Opinión (2023). https://laopinion.com/2023/03/29/tiroteo-en-nashville-cronologia-de-la-tragedia-en-the-covenant-school/

  8. HI-WATCH: HWP-N2204IH-DE3 PTZ Camera Specifications (2014). https://www.hi-watch.eu/en-us/product/2014/network-ptz/2-0-mp-4-ir-network-ptz-camera. Accessed 04 Oct 2023

  9. Koirala, A., Walsh, K.B., Wang, Z., McCarthy, C.: Deep learning - method overview and review of use for fruit detection and yield estimation. Comput. Electron. Agric. 162, 219–234 (2019)

    Article  Google Scholar 

  10. Lara, A.: 1999: matanza en la escuela de Columbine. Economist & Jurist (2022). https://www.economistjurist.es/articulos-juridicos-destacados/1999-matanza-de-la-escuela-de-columbine

  11. López-Rubio, E., Molina-Cabello, M.A., Castro, F.M., Luque-Baena, R.M., Marin-Jimenez, M.J., Guil, N.: Anomalous object detection by active search with PTZ cameras. Expert Syst. Appl. 181, 115150 (2021)

    Article  Google Scholar 

  12. Mukhina, K.D., Visheratin, A.A., Nasonov, D.: Orienteering problem with functional profits for multi-source dynamic path construction. PLoS ONE 14(4), e0213777 (2019)

    Article  Google Scholar 

  13. Olmos, R., Tabik, S., Herrera, F.: Automatic handgun detection alarm in videos using deep learning. Neurocomputing 275, 66–72 (2018)

    Article  Google Scholar 

  14. PyTorch Vision: Faster R-CNN - PyTorch Vision (2015). https://pytorch.org/vision/main/models/faster_rcnn.html. Accessed 05 Oct 2023

  15. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)

    Google Scholar 

  16. Salido, J., Lomas, V., Ruiz-Santaquiteria, J., Deniz, O.: Automatic handgun detection with deep learning in video surveillance images. Appl. Sci. 11(13), 6085 (2021)

    Article  Google Scholar 

  17. Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)

    Google Scholar 

  18. Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR (1), pp. 511–518. IEEE Computer Society (2001). http://dblp.uni-trier.de/db/conf/cvpr/cvpr2001-1.html#ViolaJ01

  19. Wightman, R.: Efficientdet-Pytorch (2022). https://github.com/rwightman/efficientdet-pytorch

Download references

Acknowledgements

This work is part of project DISARM, Grant PDC2021-121197, funded by MCIN/AEI/ 10.13039/501100011033 and “European Union NextGenerationEU/PRTR”. The work has been partially funded by the following projects: SBPLY/21/180501/000025 by the Autonomous Government of Castilla-La Mancha and the European Regional Development Fund (ERDF), and dAIEdge Grant n. 101120726 by the European Commission. Author J. Ruiz-Santaquiteria was supported by Postgraduate Grant from the Spanish Ministry of Science, Innovation, and Universities (grant number PRE2018-083772).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oscar Deniz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Muñoz, J.D., Ruiz-Santaquiteria, J., Deniz, O., Bueno, G. (2024). Weapon Detection Using PTZ Cameras. In: Filipe, J., Röning, J. (eds) Robotics, Computer Vision and Intelligent Systems. ROBOVIS 2024. Communications in Computer and Information Science, vol 2077. Springer, Cham. https://doi.org/10.1007/978-3-031-59057-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-59057-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-59056-6

  • Online ISBN: 978-3-031-59057-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics