Visibility Loss Detection for Video Camera Using Deep Convolutional Neural Networks

  • Alexey Ivanov
  • Dmitry YudinEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)


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.


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



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.


  1. 1.
    Tikhova, J.: Development sabotage detectors for surveillance systems Macroscop. Perm State National Research University (2013). Accessed 11 May 2018
  2. 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. 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. 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. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  10. 10.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  11. 11.
    Pertuz, S., Puig, D., Garcia, M.A.: Analysis of focus measure operators for shape-from-focus. Pattern Recognit. 46, 1415–1432 (2013)CrossRefGoogle Scholar
  12. 12.
    Sklearn.svm.LinearSVC. Linear Support Vector Classification. Accessed 11 May 2018
  13. 13.
    Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: CatBoost: unbiased boosting with categorical features. arXiv:1706.09516v2 (2018)
  14. 14.
    Simonyan, K., Zisserman, A.: Very Deep Convolutions for Large-Scale Image Recognition. In: ICLR. arXiv:1409.1556 (2015)
  15. 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. 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. 17.
    Kaiming, H., Xiangyu, Z., Shaoqing, R., Jian S.: Deep Residual Learning for Image Recognition. In: ECCV. arXiv:1512.03385 (2015)
  18. 18.
    Yudin, D., Zeno, B.: Event recognition on images by fine-tuning of deep neural networks. Adv. Intell. Syst. Comput. 679, 479–487 (2018)CrossRefGoogle Scholar
  19. 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. 20.
    DeepClassificationTool. Deep image classification tool based on Keras. Accessed 11 May 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Belgorod State Technological University named after V.G. ShukhovBelgorodRussia

Personalised recommendations