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Journal Article
An AIoT Monitoring System for Multi-Object Tracking and Alerting
- Authors
- Wonseok Jung, Se-Han Kim, Seng-Phil Hong, Jeongwook Seo
- Issue Date
- 2021-01
- Citation
- CMC: Computers Materials & Continua, v.637, no.1, pp.337-348
- ISSN
- 1546-2218
- Publisher
- Tech Science Press
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Accuracy performance, Automatically detecting, Bounding Box, CCTV Camera, Convolution neural network(CNN), Disease risk, Edge devices, Extraction method, Faster R-CNN, Frame Extraction, Monitoring system
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
