Journal
BIOSYSTEMS ENGINEERING
Volume 125, Issue -, Pages 98-104Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2014.07.002
Keywords
Group-housed pigs; Foreground detection; Object extraction; Mixture of Gaussians; Machine vision
Funding
- National Natural Science Foundation of China [31172243]
- Ministry of Education of China [2010322711007]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- Graduate Student Scientific Research Innovation Projects of Jiangsu Ordinary University [CXLX13_664]
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In this paper, a foreground detection method to obtain the foreground objects of pigs in overhead views of group-housed environments is proposed. The method is based on the combination of Mixture of Gaussians (MoG) using prediction mechanism (PM) and threshold segmentation algorithm. First, the valid region is manually set according to a priori knowledge. Second, the foreground objects of pigs are detected using the PM-MoG algorithm. The algorithm uses the detected binary image of the previous frame to predict the current frame in the valid region for pixels that fulfil background updating conditions. Different update strategies are used to update the background for different circumstances. Third, the maximum entropy threshold segmentation algorithm is used according to the colour information of foreground objects. Finally, the results of the two previous steps of foreground detection are fused. The experimental results show that the method is effective and can extract relatively complete foreground objects of pigs in complex scenes. These complex scenes include light changes, the influence of ground urine stains, water stains, manure, and other sundries, pigs slow movement patterns, and varying colours of foreground objects. The average foreground detection rate is approximately 92%. The experimental results set the foundation for further exploration of individual identification of group-housed pigs, their behaviour analysis, and other objectives. (C) 2014 IAgrE. Published by Elsevier Ltd. All rights reserved.
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