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.
Camera-trap, image processing, computer vision, object detection, background subtraction, convolutional neural networks, deep learning
TEKELİ, ULAŞ and BAŞTANLAR, YALIN
"Elimination of useless images from raw camera-trap data,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27:
4, Article 2.
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss4/2