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EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board

Authors
Kim, JonggwanSuh, YooilLee, JunheeChae, HeechanAhn, HanseChung, YongwhaPark, Daihee
Issue Date
Apr-2022
Publisher
MDPI
Keywords
agriculture IT; computer vision; pig counting; video object detection and tracking; convolutional neural network
Citation
SENSORS, v.22, no.7
Indexed
SCIE
SCOPUS
Journal Title
SENSORS
Volume
22
Number
7
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/140417
DOI
10.3390/s22072689
ISSN
1424-8220
1424-3210
Abstract
Knowing the number of pigs on a large-scale pig farm is an important issue for efficient farm management. However, counting the number of pigs accurately is difficult for humans because pigs do not obediently stop or slow down for counting. In this study, we propose a camera-based automatic method to count the number of pigs passing through a counting zone. That is, using a camera in a hallway, our deep-learning-based video object detection and tracking method analyzes video streams and counts the number of pigs passing through the counting zone. Furthermore, to execute the counting method in real time on a low-cost embedded board, we consider the tradeoff between accuracy and execution time, which has not yet been reported for pig counting. Our experimental results on an NVIDIA Jetson Nano embedded board show that this "light-weight" method is effective for counting the passing-through pigs, in terms of both accuracy (i.e., 99.44%) and execution time (i.e., real-time execution), even when some pigs pass through the counting zone back and forth.
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