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An AIoT Monitoring System for Multi-Object Tracking and Alerting

Wonseok Jung1, Se-Han Kim2, Seng-Phil Hong3, Jeongwook Seo4,*

1 Korea Electronics Technology Institute, Seongnam, 13509, Korea
2 Electronics and Telecommunications Research Institute, Daejeon, 34129, Korea
3 Hancom With Inc., Seongnam, 13493, Korea
4 Hanshin University, Osan-si, 18101, Korea

* Corresponding Author: Jeongwook Seo. Email: email

(This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)

Computers, Materials & Continua 2021, 67(1), 337-348. https://doi.org/10.32604/cmc.2021.014561

Abstract

Pig farmers want to have an effective solution for automatically detecting and tracking multiple pigs and alerting their conditions in order to recognize disease risk factors quickly. In this paper, therefore, we propose a novel monitoring system using an Artificial Intelligence of Things (AIoT) technique combining artificial intelligence and Internet of Things (IoT). The proposed system consists of AIoT edge devices and a central monitoring server. First, an AIoT edge device extracts video frame images from a CCTV camera installed in a pig pen by a frame extraction method, detects multiple pigs in the images by a faster region-based convolutional neural network (RCNN) model, and tracks them by an object center-point tracking algorithm (OCTA) based on bounding box regression outputs of the faster RCNN. Finally, it sends multi-pig tracking images to the central monitoring server, which alerts them to pig farmers through a social networking service (SNS) agent in cooperation with an oneM2M-compliant IoT alerting method. Experimental results showed that the multi-pig tracking method achieved the multi-object tracking accuracy performance of about 77%. In addition, we verified alerting operation by confirming the images received in the SNS smartphone application.

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Cite This Article

W. Jung, S. Kim, S. Hong and J. Seo, "An aiot monitoring system for multi-object tracking and alerting," Computers, Materials & Continua, vol. 67, no.1, pp. 337–348, 2021.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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