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