Skip to main content
Springer Nature Link
Log in

A learning-based approach for leaf detection in traffic surveillance video

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

Traffic surveillance video is recorded in uncontrolled outdoor scenarios. If the camera view gets obstructed by the leaves, the video will fail to be used in vehicle tracking and recognition. It is required that the traffic video surveillance systems run self-checking in order to evaluate if the camera view is blocked by leaves or not. In view of this, a two-step learning framework is proposed in this paper to automatically determine whether the video is leaf degraded or leaf free. First, the proposed framework exploits the convolutional neural network to learn the discriminative features of leaf particles. Then the trained model is used to detect candidate leaf patches in the image. Second, a probabilistic approach is used to pool decisions of each candidate leaf patch to generate final leaf detection result in the video. Experimental results are provided to demonstrate that the proposed approach can effectively detect leaves in real-world traffic surveillance video.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Alippi, C., Boracchi, G., Camplani, R., & Roveri, M. (2010). Detecting external disturbances on the camera lens in wireless multimedia sensor networks. IEEE Transaction on Instrumentation and Measurement, 59(11), 2982–2990.

    Article  Google Scholar 

  • Babatunde, O., Armstrong, L., Diepeveen, D., & Leng, J. (2015). A survey of computer-based vision systems for automatic identification of plant species. Journal of Agricultural Informatics, 6(1), 1–11.

    Article  Google Scholar 

  • Barbedo, J. G. A. (2015). Identifying central features of cotton leaves in digital images with difficult backgrounds. IEEE Latin America Transactions, 13(9), 3072–3079.

    Article  Google Scholar 

  • Borges, P. V. K., Mayer, J., & Izquierdo, E. (2008) A probabilistic model for flood detection in video sequences. In IEEE International Conference on Image Processing, San Diego, CA, Oct 2008 (pp. 13–16).

  • Bottou, L. (2012). Stochastic gradient descent tricks. In G. Montavon, G. B. Orr, & K.-R. Muller (Eds.), Neural Networks: Tricks of the Trade (Vol. 7700, pp. 421–436). Lecture Notes in Computer Science, Berlin: Springer.

  • Campos, Y., Sossa, H., & Pajares, G. (2016). Spatio-temporal analysis for obstacle detection in agricultural videos. Applied Soft Computing, 45, 86–97.

    Article  Google Scholar 

  • Cerutti, G., Tougne, L., Mille, J., Vacavant, A., & Coquin, D. (2013) A model-based approach for compound leaves understanding and identification. In Proceedings of the IEEE International Conference on Image Processing, Melbourne, VIC, 2013 (pp. 1471–1475).

  • Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transaction on Intelligent Systems and Technology, 2(3), 1–17.

    Article  Google Scholar 

  • Chen, Li, Zhu, Dawei, Tian, Jing, & Liu, Jiaxiang. (2016). Dust particle detection in traffic surveillance video using motion singularity analysis. Digital Signal Processing, 58, 127–133.

    Article  Google Scholar 

  • Dudek, R., Cuenca, C., & Quintana, F. (2009). An automatic optical flow based method for the detection and restoration of non-repetitive damaged zones in image sequences. In H. Badioze Zaman, P. Robinson, M. Petrou, P. Olivier, H. Schroder, & T. Shih (Eds.), Visual Informatics: Bridging Research and Practice (Vol. 5857, pp. 800–810).

  • Einecke, N., Gandhi, H., & Deigmoller, J. (2014). Detection of camera artifacts from camera images. In IEEE International Conference on Intelligent Transportation Systems, Qingdao, China, 2014 (pp. 603–610).

  • Elalami, M. E. (2014). A new matching strategy for content based image retrieval system. Applied Soft Computing, 14, 407–418.

    Article  Google Scholar 

  • Grand-Brochier, M., Vacavant, A., Cerutti, G., Kurtz, C., Weber, J., & Tougne, L. (2015). Tree leaves extraction in natural images: Comparative study of preprocessing tools and segmentation methods. IEEE Transaction on Image Processing, 24(5), 1549–1560.

    Article  MathSciNet  Google Scholar 

  • Kang, L., Ye, P., Li, Y., & Doermann, D. (2014). Convolutional neural networks for no-reference image quality assessment. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Columbus, OH, June 2014 (pp. 1733–1740).

  • Kay, S. M. (1993). Fundamentals of statistical signal processing: estimation theory. Englewood Cliffs, NJ: PTR Prentice-Hall.

    MATH  Google Scholar 

  • Kumar, N., Belhumeur, P., Biswas, A., Jacobs, D., Kress, W. J., Lopez, I. C., & Soares, J. V. B. (2012). Leafsnap: A computer vision system for automatic plant species identification. In Proceedings of the European Conference on Computer Vision, Firenze, Italy, 2012 (pp. 502–516).

  • Lin, D. -T., & Wu, C. -H. (2012). Real-time active tampering detection of surveillance camera and implementation on digital signal processor. In Interenational Conference on Intelligent Information Hiding and Multimedia Signal Processing, Piraeus, July 2012 (pp. 383–386).

  • Moorthy, A. K., & Bovik, A. C. (2010). A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 17(5), 513–516.

    Article  Google Scholar 

  • Saglam, A., & Temizel, A. (2009). Real-time adaptive camera tamper detection for video surveillance. In IEEE International Conference on Advanced Video and Signal Based Surveillance, Genova, 2009 (pp. 430–435).

  • Tian, B., Morris, B. T., Tang, M., Liu, Y., Yao, Y., Gou, C., et al. (2015). Hierarchical and networked vehicle surveillance in its: A survey. IEEE Transactions on Intelligent Transportation Systems, 16(2), 557–580.

    Google Scholar 

  • Toreyin, B. U., & Cetin, A. E. (2006). Wavelet based detection of moving tree branches and leaves in video. In IEEE International Symposium on Circuits and Systems (pp. 4, pp.–934).

  • Vedaldi, A., & Lenc, K. (2015). MatConvNet: Convolutional neural networks for MATLAB. In Proceedings of the ACM International Conference on Multimedia, Brisbane, Australia, 2015 (pp. 689–692).

  • Wang, B., Brown, D., Gao, Y., & Salle, J. L. (2013). Mobile plant leaf identification using smart-phones. In Proceedings of the IEEE International Conference on Image Processing, Melbourne, VIC, 2013 (pp. 4417–4421).

  • Wang, W., Dong, J., & Tan, T. (2009). A survey of passive image tampering detection. In A. T. S. Ho, Y. Q. Shi, H. J. Kim & M. Barni (Eds.), Digital Watermarking (Vol. 5703, pp. 308–322). Lecture Notes in Computer Science.

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61773297 and 61375017).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, China

    Li Chen, Xiaoping Peng, Jing Tian & Jiaxiang Liu

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, 430081, China

    Li Chen, Xiaoping Peng, Jing Tian & Jiaxiang Liu

Authors
  1. Li Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiaoping Peng
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jing Tian
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jiaxiang Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jing Tian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, L., Peng, X., Tian, J. et al. A learning-based approach for leaf detection in traffic surveillance video. Multidim Syst Sign Process 29, 1895–1904 (2018). https://doi.org/10.1007/s11045-017-0540-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-017-0540-6

Keywords