Skip to main content
SpringerLink
Log in

Invariant Features-Based Fuzzy Inference System for Animal Detection and Recognition Using Thermal Images

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Human–Animal Conflict (HAC) is one of the primary threats to the continued survival of animal species and it has also impacted the lives of humans drastically. In this paper, we propose an efficient animal detection and recognition system with invariant features and fuzzy logic using thermal images. The proposed system exploits various features like Zernike, shape, texture and skeleton path. Cumulatively, these features are invariant to rotation, scaling, translation, illumination, and partly posture. The proposed model is robust to several challenging image conditions like low contrast/illumination, haze/blur, occlusion, camouflage, background clutter, and poses variation. The model is tested on our thermal animal dataset that has 1862 images and 12 different animal species. Experimental results validate the significance of thermal images for animal-based applications. Besides, the proposed fuzzy system has achieved an average accuracy of 97% which is equivalent to the accuracy produced by domain experts in identifying the animals from our thermal dataset.

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

Access this article

Log in via an institution

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Ganow, K.B., Caire, W., Matlack, R.S.: Use of thermal imaging to estimate the population sizes of Brazilian free-tailed bat, Tadarida Brasiliensis, maternity roosts in Oklahoma. Southwestern Nat. 60(1), 90–96 (2015). https://doi.org/10.1894/SWNAT-D-14-00010R1.1

    Article  Google Scholar 

  2. Hristov, N.I., Betke, M., Theriault, D.E., Bagchi, A., Kunz, T.H.: Seasonal variation in colony size of Brazilian free-tailed bats at Carlsbad Cavern based on thermal imaging. J. Mammal. 91(1), 183–192 (2010). https://doi.org/10.1644/08-MAMM-A-391R.1

    Article  Google Scholar 

  3. Welbourne, D.U.S.T.E.N.: A method for surveying diurnal terrestrial reptiles with passive infrared automatically triggered cameras. PLoS ONE 6, e18965 (2013)

    Google Scholar 

  4. Goodenough, A.E., Carpenter, W.S., MacTavish, L., MacTavish, D., Theron, C., Hart, A.G.: Empirically testing the effectiveness of thermal imaging as a tool for identification of large mammals in the African bushveldt. Afr. J. Ecol. 56(1), 51–62 (2018). https://doi.org/10.1111/aje.12416

    Article  Google Scholar 

  5. Barbosa Pereira, C., Kunczik, J., Zieglowski, L., Tolba, R., Abdelrahman, A., Zechner, D., Czaplik, M.: Remote welfare monitoring of rodents using thermal imaging. Sensors 18(11), 3653 (2018). https://doi.org/10.3390/s18113653

    Article  Google Scholar 

  6. Zhou, D., Dillon, M., Kwon, E.: Tracking-based deer vehicle collision detection using thermal imaging. In: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 688–693). IEEE. (2009) https://doi.org/10.1109/robio.2009.5420589

  7. Zhou, D., Wang, J., Wang, S.: Contour based HOG deer detection in thermal images for traffic safety. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (p. 1). The steering committee of the world congress in computer science, computer engineering and applied computing (WorldComp) (2012)

  8. Cilulko, J., Janiszewski, P., Bogdaszewski, M., Szczygielska, E.: Infrared thermal imaging in studies of wild animals. Eur. J. Wildl. Res. 59(1), 17–23 (2013). https://doi.org/10.1007/s10344-012-0688-1

    Article  Google Scholar 

  9. Forslund, D., Bjärkefur, J.: Night vision animal detection. In: 2014 IEEE intelligent vehicles symposium proceedings (pp. 737–742). IEEE. (2014) https://doi.org/10.1109/ivs.2014.6856446

  10. Roy, S., Shivakumara, P., Jain, N., Khare, V., Dutta, A., Pal, U., Lu, T.: Rough-fuzzy based scene categorization for text detection and recognition in video. Pattern Recogn. 80, 64–82 (2018). https://doi.org/10.1016/j.patcog.2018.02.014

    Article  Google Scholar 

  11. Darwich, A., Hébert, P.A., Bigand, A., Mohanna, Y.: Background subtraction based on a new fuzzy mixture of Gaussians for moving object detection. J. Imaging 4(7), 92 (2018). https://doi.org/10.3390/jimaging4070092

    Article  Google Scholar 

  12. Mahapatra, A., Mishra, T. K., Sa, P. K., Majhi, B.: Background subtraction and human detection in outdoor videos using fuzzy logic. In: 2013 IEEE international conference on fuzzy systems (FUZZIEEE) (pp. 1–7). IEEE. (2013) https://doi.org/10.1109/fuzz-ieee.2013.6622397

  13. Toran, V., Sipi, D.: Fuzzy-filtered neural network for rice disease diagnosis using image analysis. Int. J. Innov. Technol. Explor. Eng. 8, 437–446 (2019)

    Google Scholar 

  14. Brunassi, L.D.A., Moura, D.J.D., Nääs, I.D.A., Vale, M.M.D., Souza, S.R.L.D., Lima, K.A.O.D., et al.: Improving detection of dairy cow estrus using fuzzy logic. Scientia Agricola 67(5), 503–509 (2010). https://doi.org/10.1590/S0103-90162010000500002

    Article  Google Scholar 

  15. John, V., Mita, S., Liu, Z., Qi, B.: . Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks. In: 2015 14th IAPR international conference on machine vision applications (MVA) (pp. 246–249). IEEE. (2015) https://doi.org/10.1109/mva.2015.7153177

  16. Kang, J.K., Hong, H.G., Park, K.R.: Pedestrian detection based on adaptive selection of visible light or far-infrared light camera image by fuzzy inference system and convolutional neural network-based verification. Sensors 17(7), 1598 (2017). https://doi.org/10.3390/s17071598

    Article  Google Scholar 

  17. Jain, I., Rani, B.: Vehicle detection using image processing and fuzzy logic. Int. J. Comput. Sci. Commun. 1(2), 255–257 (2010)

    Google Scholar 

  18. Meena, S.D., Agilandeeswari, L.: Stacked convolutional autoencoder for detecting animal images in cluttered scenes with a novel feature extraction framework. Soft computing for problem solving, pp. 513–522. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0184-5_44

    Chapter  Google Scholar 

  19. Stubendek, A., Karacs, K.: Shape recognition based on projected edges and global statistical features. Math Prob Eng (2018). https://doi.org/10.1155/2018/4763050

    Article  Google Scholar 

  20. F. systems. Thermal imaging, night vision and infrared camera system. https://www.flir.com. Accessed 3 Jan 2019

  21. Meena, S.D., Agilandeeswari, L.: An Efficient framework for animal breeds classification using semi-supervised learning and Multi-Part Convolutional Neural Network (MP-CNN). IEEE Access 7, 151783–151802 (2019). https://doi.org/10.1109/ACCESS.2019.2947717

    Article  Google Scholar 

  22. Meena, S.D., Agilandeeswari, L.: Adaboost cascade classifier for classification and identification of wild animals using movidius neural compute stick. Int. J. Eng. Adv. Technol. 9(13), 495–499 (2019). https://doi.org/10.35940/ijeat.a1089.1291s319

    Article  Google Scholar 

  23. Sun, K., Mou, S., Qiu, J., Wang, T., Gao, H.: Adaptive fuzzy control for non-triangular structural stochastic switched nonlinear systems with full state constraints. IEEE T Fuzzy Syst 27, 1587–1601 (2018). https://doi.org/10.1109/TFUZZ.2018.2883374

    Article  Google Scholar 

  24. Sun, K., Qiu, J., Karimi, H.R., Gao, H.: A novel FiniteTime control for nonstrict feedback saturated nonlinear systems with tracking error constraint. IEEE Trans. Syst. Man Cybern. Syst. (2019). https://doi.org/10.1109/tsmc.2019.2958072

    Article  Google Scholar 

  25. Divya, M.S., Agilandeeswari, L.: A new supervised clustering framework using multi discriminative parts and expectation–maximization approach for a fine-grained animal breed classification (SC-MPEM). Neural Process. Lett. (2020). https://doi.org/10.1007/s11063-020-10246-3

    Article  Google Scholar 

  26. Divya, M.S., Agilandeeswari, L.: FSSCaps-DetCountNet: fuzzy soft sets and CapsNet-based detection and counting network for monitoring animals from aerial images. J. Appl. Remote Sens. 14(2), 026521 (2020). https://doi.org/10.1117/1.JRS.14.026521

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank VIT for providing ‘VIT SEED GRANT’ to carry out this research work.

Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Divya Meena & L. Agilandeeswari

Authors
  1. Divya Meena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Agilandeeswari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. Agilandeeswari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meena, D., Agilandeeswari, L. Invariant Features-Based Fuzzy Inference System for Animal Detection and Recognition Using Thermal Images. Int. J. Fuzzy Syst. 22, 1868–1879 (2020). https://doi.org/10.1007/s40815-020-00907-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-020-00907-9

Keywords

Search

Navigation