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http://dx.doi.org/10.9717/kmms.2019.22.2.136

Separation of Occluding Pigs using Deep Learning-based Image Processing Techniques  

Lee, Hanhaesol (Dept. of Computer Convergence Software, Korea University)
Sa, Jaewon (Dept. of Computer Convergence Software, Korea University)
Shin, Hyunjun (Dept. of Computer Convergence Software, Korea University)
Chung, Youngwha (Dept. of Computer Convergence Software, Korea University)
Park, Daihee (Dept. of Computer Convergence Software, Korea University)
Kim, Hakjae (Class Act, Co., Ltd.)
Publication Information
Journal of Korea Multimedia Society / v.22, no.2, 2019 , pp. 136-145 More about this Journal
Abstract
The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs 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 occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based 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).
Keywords
Pig Monitoring; Occluding Pigs; Segmentation; Deep Learning; YOLO;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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