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Gun and Knife Detection Based on Faster R-CNN for Video Surveillance

  • M. Milagro Fernandez-CarroblesEmail author
  • Oscar Deniz
  • Fernando Maroto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11868)

Abstract

Public safety in public areas is nowadays one of the main concerns for governments and companies around the world. Video surveillance systems can take advantage from the emerging techniques of deep learning to improve their performance and accuracy detecting possible threats. This paper presents a system for gun and knife detection based on the Faster R-CNN methodology. Two approaches have been compared taking as CNN base a GoogleNet and a SqueezeNet architecture respectively. The best result for gun detection was obtained using a SqueezeNet architecture achieving a 85.44% \(AP_{50}\). For knife detection, the GoogleNet approach achieved a 46.68% \(AP_{50}\). Both results improve upon previous literature results evidencing the effectiveness of our detectors.

Keywords

Object detection Guns Knives Video surveillance 

Notes

Acknowledgments

This work was partially funded by projects TIN2017-82113-C2-2-R by the Spanish Ministry of Economy and Business and SBPLY/17/180501/000543 by the Autonomous Government of Castilla-La Mancha and the ERDF.

References

  1. 1.
    COCO dataset 2017. http://cocodataset.org. Accessed 04 May 2019
  2. 2.
    COCO detection leaderboard. http://cocodataset.org/#detection-leaderboard. Accessed 04 May 2019
  3. 3.
    Open images dataset v4. https://storage.googleapis.com/openimages/web/index.html. Accessed 04 May 2019
  4. 4.
    Weapon detection by neural network. https://github.com/Shubham02gupta/Weapon-Detection-by-Neural-network/tree/master/train. Accessed 04 May 2019
  5. 5.
    Akcay, S., Kundegorski, M.E., Willcocks, C.G., Breckon, T.P.: Using deep convolutional neural network architectures for object classification and detection within X-ray baggage security imagery. IEEE Trans. Inf. Forensics Secur. 13(9), 2203–2215 (2018).  https://doi.org/10.1109/TIFS.2018.2812196CrossRefGoogle Scholar
  6. 6.
    Dastidar, J.G., Biswas, R.: Tracking human intrusion through a CCTV. In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN), pp. 461–465, December 2015.  https://doi.org/10.1109/CICN.2015.95
  7. 7.
    Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448, December 2015.  https://doi.org/10.1109/ICCV.2015.169
  8. 8.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 580–587. IEEE Computer Society, Washington, DC (2014).  https://doi.org/10.1109/CVPR.2014.81
  9. 9.
    Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: SqueezeNet: Alexnet-level accuracy with 50x fewer parameters and \(<\)1mb model size. CoRR abs/1602.07360 (2016). http://arxiv.org/abs/1602.07360
  10. 10.
    Jehan, F., et al.: The burden of firearm violence in the United States: stricter laws result in safer states. J. Inj. Violence Res. 10(1), 11–16 (2018).  https://doi.org/10.5249/jivr.v10i1.951CrossRefGoogle Scholar
  11. 11.
    Kanehisa, R., Neto, A.: Firearm detection using convolutional neural networks. In: Proceedings of the 11th International Conference on Agents and Artificial Intelligence, ICAART, vol. 2, pp. 707–714, January 2019.  https://doi.org/10.5220/0007397707070714
  12. 12.
    Kuznetsova, A., et al.: The open images dataset V4: unified image classification, object detection, and visual relationship detection at scale. CoRR abs/1811.00982 (2018). http://arxiv.org/abs/1811.00982
  13. 13.
    Lin, T., et al.: Microsoft COCO: common objects in context. CoRR abs/1405.0312 (2015). http://arxiv.org/abs/1405.0312
  14. 14.
    Olmos, R., Tabik, S., Herrera, F.: Automatic handgun detection alarm in videos using deep learning. Neurocomputing 275, 66–72 (2018).  https://doi.org/10.1016/j.neucom.2017.05.012. http://www.sciencedirect.com/science/article/pii/S0925231217308196CrossRefGoogle Scholar
  15. 15.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: 28th International Conference on Neural Information Processing Systems, NIPS 2015, vol. 1, pp. 91–99. MIT Press, Cambridge (2015). http://dl.acm.org/citation.cfm?id=2969239.2969250
  16. 16.
    Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015.  https://doi.org/10.1109/CVPR.2015.7298594
  17. 17.
    Valldor, E., Stenborg, K.G., Gustavsson, D.: Firearm detection in social media images. In: Swedish Symposium on Deep Learning 2018, September 2018Google Scholar
  18. 18.
    Verma, G.K., Dhillon, A.: A handheld gun detection using Faster R-CNN deep learning. In: Proceedings of the 7th International Conference on Computer and Communication Technology, ICCCT-2017, pp. 84–88. ACM, New York (2017).  https://doi.org/10.1145/3154979.3154988
  19. 19.
    Yuenyong, S., Hnoohom, N., Wongpatikaseree, K.: Automatic detection of knives in infrared images. In: 2018 International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON), pp. 65–68, February 2018.  https://doi.org/10.1109/ECTI-NCON.2018.8378283
  20. 20.
    Zhang, Y., Wang, Y., Foley, J., Suk, J., Conathan, D.: Tweeting mass shootings: the dynamics of issue attention on social media. In: Proceedings of the 8th International Conference on Social Media and Society, #SMSociety 2017, pp. 59:1–59:5. ACM, New York (2017).  https://doi.org/10.1145/3097286.3097345

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • M. Milagro Fernandez-Carrobles
    • 1
    Email author
  • Oscar Deniz
    • 1
  • Fernando Maroto
    • 1
  1. 1.ETSI Industriales, VISILABUniversity of Castilla-La ManchaCiudad RealSpain

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