Automatic Identification of Flying Bird Species Using Computer Vision Technique for Ecological Data Analysis
Keywords:
bird detection, automatic, computer vision
Abstract
The aim of this study is to propose a bird detection algorithm. Bird detection is useful for the counting and dynamics of bird study. Neural networks are used for bird detection and the first step is to learn to classify bird species based on previous experiments. We further develop a proof of concept for the meta-data fusion which indicates that the fusion of elevation data can be used to increase the accuracy of the model, and to decrease its coverage error, in particular.
References
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[11]. Branson S., Van Horn G., Belongie S., Perona P., Bird species categorization using pose normalized deep convolutional nets.
[2]. Descamps S., Béchet A., Descombes X., Arnaud A., Zerubia J., An automatic counter for aerial images of aggregations of large birds, Bird Study, 58, p. 302-308, 2011.
[3]. Grenzdörffer G. J., UAS-based automatic bird count of a common gull colony, ISPRS Int. Arch. Photogramme, Remote Sens. Spat. Inform. Sci., XL-1/W2, p. 169-174, 2013.
[4]. Branson S., Van Horn G., Belongie S., Perona P., Bird species categorization using pose normalized deep convolutional nets, 2014.
[5]. Branson S., Wah C., Schroff F., Babenko B., Welinder P., Perona P., Belongie S., Visual recognition with humans in the loop, In European Conference on Computer Vision (ECCV).
[6]. Hall S. G., Price R., Wei L., Design of an Autonomous Bird Predation Reduction Device, Presented as Paper number 01-3131, at ASAE International Meeting, St. Joseph, MI, 2001.
[7]. Hall S. G., Price R. R., Mobile Semi-Autonomous Robotic Vehicles Improve Aquacultural Ventures by Reducing Bird Predation and Improving Water Quality Monitoring, Abstract, Proceedings of the World Aquaculture Society, Louisville, KY, 2003.
[8]. Lowe D. G., Distinctive image features from scale-invariant keypoints, International journal of computer vision, 60(2), p. 91-110, 2004.
[9]. Brownlee J., Object Recognition with Convolutional Neural Networks in the Keras Deep, Learning Library, Retrieved March 31, 2017, from Machine Learning Mastery, July 1, 2016.
[10]. Benenson R., Classification datasets results, Retrieved March 31, 2017, 2016, February 22.
[11]. Branson S., Van Horn G., Belongie S., Perona P., Bird species categorization using pose normalized deep convolutional nets.
Published
2019-12-15
How to Cite
1.
MARIN FB, MARIN M. Automatic Identification of Flying Bird Species Using Computer Vision Technique for Ecological Data Analysis. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Dec.2019 [cited 27Apr.2024];42(4):46-9. Available from: https://www.gup.ugal.ro/ugaljournals/index.php/mms/article/view/2818
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