[1]施宏,沈明霞,刘龙申,等.基于Kinect的哺乳期母猪姿态识别算法的研究[J].南京农业大学学报,2019,42(1):177-183.[doi:10.7685/jnau.201803025]
 SHI Hong,SHEN Mingxia,LIU Longshen,et al.Study on recognition method of lactating sows’ posture based on Kinect[J].Journal of Nanjing Agricultural University,2019,42(1):177-183.[doi:10.7685/jnau.201803025]
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基于Kinect的哺乳期母猪姿态识别算法的研究()
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《南京农业大学学报》[ISSN:1000-2030/CN:32-1148/S]

卷:
42卷
期数:
2019年1期
页码:
177-183
栏目:
食品与工程
出版日期:
2019-01-09

文章信息/Info

Title:
Study on recognition method of lactating sows’ posture based on Kinect
作者:
施宏1 沈明霞1 刘龙申1 陆明洲1 孙玉文1 刘志刚2
1. 南京农业大学工学院/江苏省智能化农业装备重点实验室, 江苏 南京 210031;
2. 南通科技职业学院, 江苏 南通 226007
Author(s):
SHI Hong1 SHEN Mingxia1 LIU Longshen1 LU Mingzhou1 SUN Yuwen1 LIU Zhigang2
1. College of Engineering/Jiangsu Key Laboratory for Intelligent Agricultural Equipment, Nanjing Agricultural University, Nanjing 210031, China;
2. Nantong Vocational College of Science and Technology, Nantong 226007, China
关键词:
Kinect小梅山母猪姿态识别DBSCAN密度聚类脊背提取
Keywords:
KinectXiaomeishan sowposture recognitionDBSCAN density clustering algorithmback extraction
分类号:
S126
DOI:
10.7685/jnau.201803025
摘要:
[目的]哺乳期母猪的姿态是其母性的外在表现,为监测母猪在哺乳期的哺乳行为,提出了一种基于Kinect的无接触式母猪姿态识别算法。[方法]使用Kinect 2.0采集位于限位栏内哺乳期小梅山母猪的深度数据。先通过姿态预分类将母猪的姿态分为站姿与卧姿,而后针对卧姿,使用基于DBSCAN(density-based spatial clustering of applications with noise)密度聚类算法计算母猪高度信息的簇数,通过比较簇的个数将卧姿分为侧卧与趴卧;针对站姿,使用基于脊背线提取的识别算法,将脊背线分成前后2段,通过比较前后2段脊背线的平均高度将站姿分为站立与坐立。[结果]比较人眼观察结果与算法识别结果,该算法识别站立、坐立的准确率分别为94.3%、92.6%,趴卧识别准确率为84.2%,侧卧姿态为93.7%。[结论]提出了一种无接触式的哺乳期母猪姿态识别算法,为母猪哺乳能力的评判与健康状况的分析提供了技术支持。
Abstract:
[Objectives]Sow posture is an important welfare indicator with regards to the maternal behavior. This paper proposes a non-contact sow posture recognition algorithm based on Kinect to monitor the maternal behavior of sows during the lactating period. [Methods]Kinect 2.0 was used to collect depth data from Xiaomeishan sows in a farrowing pen. Firstly,the posture of the sow was divided into two categories:standing and lying. Based on the DBSCAN(density-based spatial clustering of applications with noise) density clustering algorithm,the cluster number of the height information of the sow was calculated. The lying posture was grouped into lateral lying and the sternal lying depending on the number of the clusters. For standing posture,a recognition algorithm based on dorsal extraction was implemented,the back line was divided into two segments by the average of the height data of the two back lines,and standing posture was further divided into standing and sitting posture. [Results]Compared the results of algorithm recognition to human eye observation,the proposed system achieved an accuracy of 94.3% for standing,92.6% for sitting,84.2% for sternal lying and 93.7% for lateral lying. [Conclusions]In this paper,a non-contact behavior monitoring method was proposed,which can provide reliable technological support for the evaluation of sow health status and a stockman support system.

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相似文献/References:

[1]陶源栋,沈明霞,刘龙申,等.基于Kinect的母猪呼吸频率测定算法[J].南京农业大学学报,2017,40(5):921.[doi:10.7685/jnau.201701032]
 TAO Yuandong,SHEN Mingxia,LIU Longshen,et al.Study on measurement algorithm of sow respiratory frequency based on Kinect[J].Journal of Nanjing Agricultural University,2017,40(1):921.[doi:10.7685/jnau.201701032]

备注/Memo

备注/Memo:
收稿日期:2018-03-14。
基金项目:政府间国际科技创新合作重点专项(2017YFE0114400);南通市市级科技计划(指导性)项目(YYZ16032)
作者简介:施宏,硕士研究生。
通信作者:沈明霞,教授,博导,主要从事机器视觉与信息农业研究,E-mail:mingxia@njau.edu.cn。
更新日期/Last Update: 1900-01-01