Real-Time Identification of Animals found in Domestic Areas of Europe

摘要

This paper presents a method for identifying 34 animal classes corresponding to the most conventional animals foundin the domestic areas of Europe by using four types of Convolutional Neural Networks (CNNs), namely VGG-19,InceptionV3, ResNet-50, and MobileNetV2. We also built a system capable of classifying all these 34 animal classesfrom images as well as in real-time from videos or a webcam. Additionally, our system is capable to automaticallygenerate two new datasets, one dataset containing textual information (i.e. animal class name, date and time intervalwhen the animal was present in the frame) and one dataset containing images of the animal classes present and identifiedin videos or in front of a webcam. Our experimental results show a high overall test accuracy for all 4 proposedarchitectures (90.56% for VGG-19 model, 93.41% for InceptionV3 model, 93.49 for ResNet-50 model and 94.54% forMobileNetV2 model), proving that such systems enable an unobtrusive method for gathering a rich collection ofinformation about the vast numbers of animal classes that are being identified such as providing insights about whatanimal classes are present at a given date and time in a certain area and how they look, resulting in valuable datasetsespecially for researchers in the area of ecology.