Real-time identification of animals found in domestic areas of Europe

Sorin Liviu Jurj; Flavius Opritoiu; Mircea Vladutiu

doi:10.1117/12.2556376

31 January 2020 Real-time identification of animals found in domestic areas of Europe

Sorin Liviu Jurj, Flavius Opritoiu, Mircea Vladutiu

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143313 (2020) https://doi.org/10.1117/12.2556376
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

This paper presents a method for identifying 34 animal classes corresponding to the most conventional animals found in 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 classes from images as well as in real-time from videos or a webcam. Additionally, our system is capable to automatically generate two new datasets, one dataset containing textual information (i.e. animal class name, date and time interval when the animal was present in the frame) and one dataset containing images of the animal classes present and identified in videos or in front of a webcam. Our experimental results show a high overall test accuracy for all 4 proposed architectures (90.56% for VGG-19 model, 93.41% for InceptionV3 model, 93.49 for ResNet-50 model and 94.54% for MobileNetV2 model), proving that such systems enable an unobtrusive method for gathering a rich collection of information about the vast numbers of animal classes that are being identified such as providing insights about what animal classes are present at a given date and time in a certain area and how they look, resulting in valuable datasets especially for researchers in the area of ecology

Citation Download Citation

Sorin Liviu Jurj, Flavius Opritoiu, and Mircea Vladutiu "Real-time identification of animals found in domestic areas of Europe", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143313 (31 January 2020); https://doi.org/10.1117/12.2556376

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