Design and implementation of in-situ human-elephant conflict management system

Abstract

Elephant play a major role in maintaining the ecosystem. Elephants move out of their corridor in search of food and water, result in rise of human-elephant conflicts. Human-Elephant conflict arises in different form such as destruction of field by elephants, elephants are runover by train, elephants getting electrocuted etc. Prevention of elephants entering into human living areas, will reduce the human-elephant conflict to a greater extent. In this paper, performance of conventional image processing techniques, custom Convolutional Neural Network (CNN) architecture and transfer learning based CNN architectures is investigated in the context of human-elephant conflict management system. On identification of elephant from the acquired video feed, the sounds (humming of Bee, Tiger growls) are generated to mitigate the progress of elephant into human living areas. It is observed that the convolutional neural networks produced a higher accuracy or prediction rate compared to conventional image processing technique. It is also observed that, VGG16 CNN model produced an accuracy of 94% with an average computation time varying between 1.5 to 2.1 s. For real time implementation, SqueezeNet CNN model is used because of its lower computation time (varying between 0.02 to 0.05 s) and moderate accuracy of 92.67%.