Skip to main content
SpringerLink
Log in

Performance optimization of rotation-tolerant Viola–Jones-based blackbird detection

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

The research described in this paper investigates the rotational robustness of the Viola–Jones algorithm (VJA) object detection method when used for red-winged blackbird (Agelaius phoeniceus) detection. VJA has been successfully used for face detection, but can be adapted to detect a variety of objects. This work uses the histogram of oriented gradients (HOG) descriptor to train the blackbird classifier. Since VJA object detection is inherently not invariant to in-plane object rotation, additional effort is required during training and detection. The proposed method extends the object detection framework developed by Viola and Jones to efficiently handle rotated blackbirds and provide a balance between detection accuracy and computation cost.

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

Similar content being viewed by others

References

  1. Klug, P.E.: Chapter 13: the future of blackbird management research. In: Ecology and management of blackbird (Icteridae) in North America, pp 219–229. USDA National Wildlife Research Center-Staff Publications 2006 (2017)

  2. Linz, G.M., Homan, H.J., Werner, S.J., Hagy, H.M., Bleier, W.J.: Assessment of bird-management strategies to protect sunflowers. American Institute of Biological Sciences, Washington, DC (2011) (electronic ISSN 1525-3244)

    Book  Google Scholar 

  3. Klosterman, M.E.: Comparisons between blackbird damage to corn and sunflower in North Dakota. Crop Protect 53, 1–5 (2013) (Elsevier)

    Article  Google Scholar 

  4. Dalal, N., Triggs, B.: Histograms of oriented gradients of human detection. Comput Vis Pattern Recognit IEEE, Montbonnot, 25 July 2005

  5. Wang, Y.: An analysis of the Viola–Jones face detection algorithm. Image Process on line, IPOL J ENS Cachan, France, pp 128–148 (2014)

  6. Anil, R.: Bird’s eye view: detecting and recognizing birds using the BIRDS 200 dataset. University of California, San Diego (2011)

    Google Scholar 

  7. Kolsch, M., Turk, M.: Analysis of rotational robustness of hand detection with a Viola–Jones detector. In: Proceedings of the 17th International Conference on Pattern Recognition (2004)

  8. Huang, C., Ai, H., Li, Y., Lao, S.: High-performance rotation invariant multiview face detection. IEEE Trans Pattern Anal Mach Intell 29(4), 671–686 (2007)

    Article  Google Scholar 

  9. Acasandrei, L., Barriga, A.: AMBA bus hardware accelerator IP for Viola–Jones face detection. IET Comput Digit Tech 7, 200–209 (2013)

  10. Ren, S., Deligiannis, N., Andreopoulos, Y.: Dynamic scheduling for energy minimization in delay-sensitive stream mining. IEEE Trans Signal Process 62(20), 5439–5448 (2014)

    Article  MathSciNet  Google Scholar 

  11. Wang, W.: Parallelization and performance optimization on face detection algorithm with OpenCL: a case study, vol 17(3). Tsinghua Science and Technology (2012)

  12. Ampatzidis, Y., Ward, J., Samara, O.: Autonomous system for pest bird control in specialty crops using unmanned aerial vehicles. ASABE annual international meeting, paper # 152181748, 26–29 July (2015)

  13. Christie, K.S., Gilbert, S.L., Brown, C.L., Hatfield, M., Hanson, L.: Unmanned aircraft systems in wildlife research: current and future applications of a transformative technology. Front Ecol Environ 14, 241–251 (2016)

    Article  Google Scholar 

  14. Jones, M., Viola, P.: Fast multi-view face detection. Mitsubishi Electric Research Laboratories, MERL, Cambridge, MA (2003)

    Google Scholar 

  15. http://www.inpixal.com/en/. Accessed 7 Mar 2017

  16. Lowe, D.G.: Distinctive image features from scale-invariant key points. Int. J. Comput. Vis., Springer, Vancouver, pp 91–110 (2004)

  17. Ai, H.: Vector boosting for rotation invariant multi-view face detection. In: Conference: Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, vol 1 (2005)

  18. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer vision and pattern recognition, CVPR 2001 (2001)

  19. Lienhart, R.: An extended set of Haar-like features for rapid object detection. Image processing. International Conference on Image Processing (2002)

  20. Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Patt. Anal. Mach. Intell. 20, 22–38 (1998)

    Google Scholar 

  21. Schneiderman, H., Kanade, T.: A statistical method for 3D object detection applied to faces and cars. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000 (2000)

  22. Karami, E., Prasad, S., Shehata, M.: Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. In: Proceedings of the 2015 Newfoundland Electrical and Computer Engineering, Conference, St. johns, Canada, November (2015)

Download references

Acknowledgements

This work was supported by a cooperative agreement with the US Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center (QA #2348). Reference to trade names does not imply endorsement of commercial products or exclusion of similar products by the US government. Research was conducted with approval from the North Dakota State University Institutional Animal Care and Use Committee (IACUC #A14068) and under permits from the US Fish and Wildlife Scientific Collecting Permit (#MB39327B-0) and the North Dakota Game and Fish Department (#GNF03799268). Access to the birds for photographing was provided by Lucas Wandrie.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND, USA

    Nauman Jalil, Scott C. Smith & Roger Green

Authors
  1. Nauman Jalil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Scott C. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roger Green
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nauman Jalil.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jalil, N., Smith, S.C. & Green, R. Performance optimization of rotation-tolerant Viola–Jones-based blackbird detection. J Real-Time Image Proc 17, 471–478 (2020). https://doi.org/10.1007/s11554-018-0795-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-018-0795-7

Keywords

Search

Navigation