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

Performance optimization of rotation-tolerant Viola–Jones-based blackbird detection 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 detec- tion, 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. Keywords Viola–Jones algorithm · Histogram of oriented gradients (HOG) · In-plane object rotation · Region of interest (ROI) · Gradient orientation 1 Introduction commodity. Nationally, blackbird damage to sunflower crops exceeds $13 million dollars. The most limiting factor on the Modern development has been destroying the habitat of production of sunflowers is damage caused by blackbirds, many species, including pest birds, forcing them to rely which generally rank third behind insects and plant spacing more and more on farmed crops for survival, resulting in [3]; however, blackbirds cannot be easily controlled, unlike crop loss. In Northern http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Real-Time Image Processing Springer Journals

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

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Image Processing and Computer Vision; Multimedia Information Systems; Computer Graphics; Pattern Recognition; Signal,Image and Speech Processing
ISSN
1861-8200
eISSN
1861-8219
D.O.I.
10.1007/s11554-018-0795-7
Publisher site
See Article on Publisher Site

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 detec- tion, 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. Keywords Viola–Jones algorithm · Histogram of oriented gradients (HOG) · In-plane object rotation · Region of interest (ROI) · Gradient orientation 1 Introduction commodity. Nationally, blackbird damage to sunflower crops exceeds $13 million dollars. The most limiting factor on the Modern development has been destroying the habitat of production of sunflowers is damage caused by blackbirds, many species, including pest birds, forcing them to rely which generally rank third behind insects and plant spacing more and more on farmed crops for survival, resulting in [3]; however, blackbirds cannot be easily controlled, unlike crop loss. In Northern

Journal

Journal of Real-Time Image ProcessingSpringer Journals

Published: Jun 4, 2018

References

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