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Title: Elimination of useless images from raw camera-trap data
Authors: Tekeli, Ulaş
Baştanlar, Yalın
Tekeli, Ulaş
Baştanlar, Yalın
Izmir Institute of Technology. Electronics and Communication Engineering
Keywords: Camera-trap
image processing
computer vision
object detection
background subtraction
convolutional neural networks
deep learning
Issue Date: 2019
Publisher: Türkiye Klinikleri Journal of Medical Sciences
Abstract: Camera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast Fourier transform. To eliminate the images without animals, we propose a system combining convolutional neural networks and background subtraction. We experimentally show that the proposed approach keeps 99% of photos with animals while eliminating more than 50% of photos without animals. We also present a software prototype that employs developed algorithms to eliminate useless images.
Description: Tekeli, Ulas/0000-0003-0492-3059
WOS: 000482742800002
ISSN: 1300-0632
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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