An elephant detection system to prevent human-elephant conflict and tracking of elephant using deep learning

Abstract

Human settlement is spreading to forest boundary areas because of the population growth, it triggers disputes between elephants and humans, leading to the loss of property and life. Continuous monitoring and tracking of elephants are difficult due to their large size and movement. Therefore, large-scale for real-time detection and alert of elephant intrusion into human settlements, monitoring is needed. Many methods had been implemented for the elephant’s intrusion detection and warning systems. Wildlife conservation and the management of human-elephant conflict require a cost-effective method of monitoring elephant behavior. In this paper, a method for the identification of the elephant as an object using image processing is proposed. The major aim of the study is to minimize the human-elephant conflict in the forest border areas and the conservation of elephants from human activities as well as protect human lives from elephant attacks. We used a data set containing elephants and we developed an approach to distinguish elephants and other animals. We used the Convolutional Neural Network and achieved a maximum accuracy of 94 percent. The proposed method outperformed existing approaches and robustly and accurately detected elephants. It thus can form the basis for a future automated early warning system for elephants.