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A layered approach to detect elephants in live surveillance video streams using convolution neural networks

Publication Type : Journal Article

Publisher : IOS Press

Source : Journal of Intelligent & Fuzzy Systems, IOS Press, Volume 38, p.6291 - 6298 (2020)

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Keywords : Human elephant conflict, machine learning, convolutional neural network, support vector machine

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2020

Abstract : Human-Elephant Conflict (HEC) and its mitigation have always been a serious conservation issue in India. It occurs mainly due to the encroachment of forests by humans as part of societal development. Consequently, these human settlements are highly affected by the intrusion of wild elephants as they cause extensive crop-raiding, injuries and even death in many cases. HEC is a growing problem in rural areas of India which shares a border with forests and other elephant habitats. Based on the studies, it is very explicit that HEC is an important conservation issue which affects the peaceful co-existence of both humans and elephants near the forest areas. The desirable solution for this problem would be to facilitate co-existence among humans and elephants, but this often fails because of technical difficulties. Hence, this paper presents an end-to-end technological solution to facilitate smoother coexistence of humans and elephants. The proposed work deploys a live video surveillance system along with deep learning strategies to effectively detect the presence of elephants. From the numerical analysis, it is revealed that the post-training accuracy of the deep learning model used in the proposed approach is evaluated at 98.7% and outperforms an an out-of-the-box image detector. The layered app

Cite this Research Publication : S. Ravikumar, Vinod, D., Dr. Gowtham R., Pulari, S. Raj, and Dr. Senthil Kumar M., “A layered approach to detect elephants in live surveillance video streams using convolution neural networks”, Journal of Intelligent & Fuzzy Systems, vol. 38, pp. 6291 - 6298, 2020.

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