Animal Specie Detection Using Deep Convolution Neural Network

Abstract

Abstract: India is a diverse country in term of wild life, all kind of wild life animal in north and southern part of India like Bengal Tiger, Rhino, Leopard are some of them and many of them are in stage of extinct, only few of them are remaining so to check their population continuous monitoring and research is important. To monitor in forest one to one is very tedious and time consuming job so technology have evolved which include installing cameras in animal living areas and acquire videos and pictures, hence save time and money. The images received in camera's not recognisable in general as pictures are blurred , without animals, or difficult to detect, resulting in doubt and error. To cater this obstacle, we suggest a database driven network captured images with dataset of different animal species stored from spatiotemporal domain and pictures with animal are captured from forest are compared with the dataset, identify if a graph matches the animal or not. This animal detection model have developed by us based on self training by Deep Neural Network (DCNN). The technology is used for classification using ML and AI algorithms, support vector machine, k-mean nearest neighbour, and ensemble tree. This suggest system can accurately classify pictures up to 91% accuracy. Keywords: Installed Camera, Animal detection ·Camera images·DCNN, ·Deep learning ·SVM ·KNN