Skip to main content

Visibility Loss Detection for Video Camera Using Deep Convolutional Neural Networks

  • 262 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 874)

Abstract

The article describes the application of various machine learning methods for the analysis of images obtained from a video camera with the purpose of detection its partial or total visibility loss. Computational experiments were performed on a data set containing more than 6800 images. Support vector machine, categorical boosting and simplified modifications of VGG, ResNet, InceptionV3 architectures of neural networks are used for image classification. A comparison of the methods quality is presented. The best results in terms of classification accuracy are obtained using ResNetm and InceptionV3m architectures. The recognition accuracy is on the average more than 96%. The processing time per reduced input frame is 8–12 ms. The obtained results confirm the applicability of the proposed approach to the detection of camera visibility loss for real tasks arising in on-board machine vision systems and video surveillance systems.

Keywords

  • Image recognition
  • Convolutional neural network
  • Deep learning
  • Support vector machine
  • Boosting
  • Classification
  • Visibility loss
  • Video camera

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-01818-4_43
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-01818-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

References

  1. Tikhova, J.: Development sabotage detectors for surveillance systems Macroscop. Perm State National Research University (2013). https://www.scienceforum.ru/2013/pdf/7653.pdf. Accessed 11 May 2018

  2. Aksay, A., Temizel, A., Cetin, A.E.: Camera tamper detection using wavelet analysis for video surveillance. In: Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, London, UK, pp. 558–562 (2007)

    Google Scholar 

  3. Mantini, P., Shah, S.K.: A signal detection theory approach for camera tamper detection. In Proceedings of the 14th IEEE International Conference on Advanced Video and Signal based Surveillance, Lecce, Italy (2017)

    Google Scholar 

  4. Einecke, N., Gandhi, H., Deigmöller, J.: Detection of camera artifacts from camera images. In: IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pp. 603–610 (2014)

    Google Scholar 

  5. Gallen, R., Cord, A., Hautière, N., Aubert, D.: Towards night fog detection through use of in-vehicle multipurpose cameras. Intelligent Vehicles Symposium (IV). IEEE (2011)

    Google Scholar 

  6. Miclea, R.-C., Silea, I.: Visibility detection in foggy environment. In: 20th International Conference on Control Systems and Computer Science (CSCS), pp. 959–964 (2015)

    Google Scholar 

  7. Alami, S., Ezzine, A., Elhassouni, F.: Local fog detection based on saturation and RGB-correlation. In: 13th International Conference on Computer Graphics Imaging and Visualization (CGiV) (2016)

    Google Scholar 

  8. Pavlic, M., Rigoll, G., Ilic, S.: Classification of images in fog and fog-free scenes for use in vehicles. In: Intelligent Vehicles Symposium (IV), pp. 481–486. IEEE (2013)

    Google Scholar 

  9. Hautiere, N., Tarel, J.-P., Aubert, D.: Mitigation of visibility loss for advanced camera-based driver assistance. IEEE Trans. Intell. Transp. Syst. 11(2), 474–484 (2010)

    CrossRef  Google Scholar 

  10. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    CrossRef  Google Scholar 

  11. Pertuz, S., Puig, D., Garcia, M.A.: Analysis of focus measure operators for shape-from-focus. Pattern Recognit. 46, 1415–1432 (2013)

    CrossRef  Google Scholar 

  12. Sklearn.svm.LinearSVC. Linear Support Vector Classification. http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html. Accessed 11 May 2018

  13. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: CatBoost: unbiased boosting with categorical features. arXiv:1706.09516v2 (2018)

  14. Simonyan, K., Zisserman, A.: Very Deep Convolutions for Large-Scale Image Recognition. In: ICLR. arXiv:1409.1556 (2015)

  15. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 1, pp. 1097–1105 (2012)

    Google Scholar 

  16. Yudin, D., Knysh, A.: Vehicle recognition and its trajectory registration on the image sequence using deep convolutional neural network. In: The International Conference on Information and Digital Technologies, pp. 435–441 (2017)

    Google Scholar 

  17. Kaiming, H., Xiangyu, Z., Shaoqing, R., Jian S.: Deep Residual Learning for Image Recognition. In: ECCV. arXiv:1512.03385 (2015)

  18. Yudin, D., Zeno, B.: Event recognition on images by fine-tuning of deep neural networks. Adv. Intell. Syst. Comput. 679, 479–487 (2018)

    CrossRef  Google Scholar 

  19. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: ECCV. arXiv:1512.00567 (2016)

  20. DeepClassificationTool. Deep image classification tool based on Keras. https://github.com/yuddim/deepClassificationTool. Accessed 11 May 2018

Download references

Acknowledgment

Research is carried out with the financial support of The Ministry of Education and Science of the Russian Federation within the Public contract project 2.1396.2017/4.6.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dmitry Yudin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ivanov, A., Yudin, D. (2019). Visibility Loss Detection for Video Camera Using Deep Convolutional Neural Networks. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 874. Springer, Cham. https://doi.org/10.1007/978-3-030-01818-4_43

Download citation