Recognition Of Animal Species On Camera Trap Images Using Machine Learning And Deep Learning Models

摘要

Wild animal movement monitoring and its distribution are essential for the conservation of animal life. Camera trap, a most commonly used technique for animal monitoring which automatically activate the camera on animal presence and obtain a huge volume of data. The present work aims to investigate various machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF) and deep learning models such as Alexnet, Inception V3 for classification of animal species. Among which deep learning models outperforms than machine learning algorithms. In this paper, the overall comparison of accuracy between machine learning and deep learning models has been observed and discussed. The outcomes of the experiment suggest that InceptionV3 attains more accuracy than SVM, Random Forest, AlexNet and also results highly accurate classification is obtained with the availability of enough data and precise techniques. The experiment uses KTH dataset that composed of 19 different categories of animals among which 12 classes are selected to measure the performance of the models.