Year: 2025
Type: conference-paper
Abstract: We classified common dog activities, such as sitting, standing, and lying down, which are crucial for monitoring the well-being of pets. To create a new model, we used convolutional neural networks (CNNs) on a Raspberry Pi platform and the InceptionV3 model, optimized on a dataset of Siberian Husky photos. The accuracy was 88% on a test set of 50 samples. In the developed model, TensorFlow Keras was used, while the OpenCV library was also used for system interaction with the Raspberry Pi and its Camera module. The model was effective for the image classification of dog behaviors in various environmental circumstances. The model substantially contributes to the development of pet welfare monitoring systems and improves the care for beloved animal companions.
Authors: Evenizer Nolasco, Anton Caesar Aldea, Jocelyn Villaverde
Institution: Mapúa University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409965008