Title: Deep Learning-based Overlapping-Pigs Separation by Balancing Accuracy and Execution Time
Authors: Lee, Hanhaesol
Sa, Jaewon
Chung, Yongwha
Park, Daihee
Kim, Hakjae
Citation: WSCG 2019: full papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 17-25.
Issue Date: 2019
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: http://hdl.handle.net/11025/35605
ISBN: 978-80-86943-37-4 (CD/-ROM)
ISSN: 2464–4617 (print)
2464-4625 (CD/DVD)
Keywords: sledování prasat;překrývající se prasata;oddělení;hluboké učení;YOLO;You Only Look Once
Keywords in different language: pig monitoring;overlapping-pigs;separation;deep learning;YOLO;You Only Look Once
Abstract in different language: The crowded environment of a pig farm is highly vulnerable to the spread of infectious diseases such as foot-andmouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a top-view camera. Although it is required to correctly separate overlapping-pigs for tracking each individual pigs, extracting the boundaries of each pig fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate overlapping-pigs not only by exploiting the advantage (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the disadvantage (i.e., the axis aligned bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with the occlusion patterns between the overlapping-pigs show that the proposed method can provide better accuracy and faster processing speed than one of the state-of-the-art deep learningbased segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG 2019: Full Papers Proceedings

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