Advertisement

Pattern Recognition and Image Analysis

, Volume 28, Issue 4, pp 605–611 | Cite as

Detecting Animals in Infrared Images from Camera-Traps

  • P. Follmann
  • B. Radig
Proceedings of the 6th International Workshop
  • 3 Downloads

Abstract

Camera traps mounted on highway bridges capture millions of images that allow investigating animal populations and their behavior. As the manual analysis of such an amount of data is not feasible, automatic systems are of high interest. We present two different of such approaches, one for automatic outlier classification, and another for the automatic detection of different objects and species within these images. Utilizing modern deep learning algorithms, we can dramatically reduce the engineering effort compared to a classical hand-crafted approach. The results achieved within one day of work are very promising and are easily reproducible, even without specific computer vision knowledge.

Keywords

wildlife monitoring animal detection outlier classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. M. Rowcliffe and C. Carbone, “Surveys using cameratraps: are we looking to a brighter future?”, Anim. Conserv. 11 (3), 185–186 (2008).CrossRefGoogle Scholar
  2. 2.
    B. Radig and P. Follmann, “Training a classifier for automatic flash detection in million images from camera–traps,” in Proc. ICPRAI 2018–Int. Conf. on Pattern Recognition and Artificial Intelligence, Workshop on Image Mining–Mathematical Theory and Applications (Montreal, Canada, 2018), pp. 589–591.Google Scholar
  3. 3.
    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Int. Conf. Advances in Neural Information Processing Systems 25 (NIPS 2012) (Lake Tahoe, NV, 2012), pp. 1097–1105.Google Scholar
  4. 4.
    B. Barz and J. Denzler, “Deep learning is not a matter of depth but of good training,” in Proc. ICPRAI 2018–Int. Conf. on Pattern Recognition and Artificial Intelligence, Workshop on Image Mining–Mathematical Theory and Applications (Montreal, Canada, 2018), pp. 683–687.Google Scholar
  5. 5.
    C.–A. Brust. T. Burghardt, et al., “Towards automated visual monitoring of individual gorillas in the wild,” in Proc. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) (Venice, Italy, 2017), pp. 2820–2830.CrossRefGoogle Scholar
  6. 6.
    S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster RCNN: Towards real–time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39 (6), 62–66 (2017).CrossRefGoogle Scholar
  7. 7.
    T.–Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proc. 2017 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (Honolulu, HI, 2017), pp. 936–944.CrossRefGoogle Scholar
  8. 8.
    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. 2016 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (Las Vegas, NV, 2016), pp. 770–778.CrossRefGoogle Scholar
  9. 9.
    T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proc. 2017 IEEE International Conference on Computer Vision (ICCV) (Venice, Italy, 2017), pp. 2999–3007.Google Scholar
  10. 10.
    R. Girshick, I. Radosvovic, G. Gkioxari, P. Dollár, K. He, Detectron (2018) https://github.com/facebookresearch/detectron.Google Scholar
  11. 11.
    T. Y. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: common objects in context,” in Computer Vision–ECCV 2014, Proc. 13th European Conf. on Computer Vision, Part V, Ed. by D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Lecture Notes in Computer Science (Springer, Cham, 2013), Vol. 8693, pp. 740–755.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Faculty of InformaticsTechnical University of MunichMunichGermany
  2. 2.ResearchMVTec Software GmbHMunichGermany

Personalised recommendations