An Automated Animal Classiﬁcation System: A Transfer Learning Approach
8 Pages Posted: 27 Feb 2020 Last revised: 2 Mar 2020
Date Written: February 27, 2020
Animal classiﬁcation from images obtained by various techniques in forest become an important task to carry out focused distribution and abundance estimation. In the following paper a frame work based on Transfer Learning (TL) in a Convolutional Neural Network is proposed for the construction of an automated animal identiﬁcation system. The framework is used to analyze & identify focal species in the images. A dataset of 6,203 camera trap images of 11 species including Wild pig, Barking deer, Chital, Elephant, Gaur, Hare, Jackal, Jungle cat, Porcupine, Sambhar, Sloth bear was obtained. Superior performance can be achieved by using Transfer learning in Deep Convolutions Neural Network (DCNN) for species classiﬁcation. The accuracy achieved by the proposed model on the test dataset is 96% in 18 epochs by using batch-size of 32. This, in turn, can speed up research ﬁndings, construct more eﬃcient and reliable animal monitoring systems, and consequently, save the time and eﬀort of the Indian scientists. Therefore, having the potential to make signiﬁcant impacts in the classiﬁcation and analysis of camera trap images of the site under observation.
Keywords: Abundance, Distribution, Images, Convolutions Neural Network, Deep Learning, Transfer Learning, Animal Classification, VGG-16
Suggested Citation: Suggested Citation