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http://dx.doi.org/10.6109/jkiice.2020.24.1.22

Design of YOLO-based Removable System for Pet Monitoring  

Lee, Min-Hye (Department of Computer Information Engineering, Kunsan National University)
Kang, Jun-Young (Department of Electronic Engineering, Wonkwang University)
Lim, Soon-Ja (Department of Electronic Engineering, Wonkwang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.1, 2020 , pp. 22-27 More about this Journal
Abstract
Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time.
Keywords
Pets; YOLO; Object Detection; Tracking; Monitoring;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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