Skeleton extraction and pose estimation of piglets using ZS-DLC-PAF

IF 2.2 2区农林科学 Q2 AGRICULTURAL ENGINEERING

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI:10.25165/j.ijabe.20231603.6930

Chengqi Liu, Haijian Ye, Shuhan Lu, Zhan Tang, Zhao Bai, Lei Diao, Longhe Wang, Lin Li

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引用次数: 0

Abstract

The accurate identification of various postures in the daily life of piglets that are directly reflected by their skeleton morphology is necessary to study the behavioral characteristics of pigs. Accordingly, this study proposed a novel approach for the skeleton extraction and pose estimation of piglets. First, an improved Zhang-Suen (ZS) thinning algorithm based on morphology was used to establish the chain code mechanism of the burr and the redundant information deletion templates to achieve a single-pixel width extraction of pig skeletons. Then, body nodes were extracted on the basis of the improved DeepLabCut (DLC) algorithm, and a part affinity field (PAF) was added to realize the connection of body nodes, and consequently, construct a database of pig behavior and postures. Finally, a support vector machine was used for pose matching to recognize the main behavior of piglets. In this study, 14 000 images of piglets with different types of behavior were used in posture recognition experiments. Results showed that the improved algorithm based on ZS-DLC-PAF achieved the best thinning rate compared with those of distance transformation, medial axis transformation, morphology refinement, and the traditional ZS algorithm. The node tracking accuracy reached 85.08%, and the pressure test could accurately detect up to 35 nodes of 5 pigs. The average accuracy of posture matching was 89.60%. This study not only realized the single-pixel extraction of piglets’ skeletons but also the connection among the different behavior body nodes of individual sows and multiple piglets. Furthermore, this study established a database of pig posture behavior, which provides a reference for studying animal behavior identification and classification and anomaly detection. Keywords: piglets, skeleton extraction, pose estimation, Zhang-Suen, DeepLabCut, Part affinity field DOI: 10.25165/j.ijabe.20231603.6930 Citation: Liu C Q, Ye H J, Lu S H, Tang Z, Bai Z, Diao L, et al. Skeleton extraction and pose estimation of piglets using ZS-DLC-PAF. Int J Agric & Biol Eng, 2023; 16(3): 180–193.

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基于ZS-DLC-PAF的仔猪骨骼提取与位姿估计

准确识别仔猪日常生活中由其骨骼形态直接反映的各种姿势，是研究猪的行为特征所必需的。因此，本研究提出了一种新的仔猪骨骼提取和姿态估计方法。首先，采用改进的基于形态学的张孙(Zhang-Suen, ZS)细化算法，建立毛刺和冗余信息删除模板的链编码机制，实现猪骨架的单像素宽度提取;然后，基于改进的DeepLabCut (DLC)算法提取身体节点，并加入部位亲和场(PAF)实现身体节点的连接，从而构建猪的行为和姿态数据库。最后，利用支持向量机进行姿态匹配，识别仔猪的主要行为。本研究采用14000张不同行为类型的仔猪图像进行姿势识别实验。结果表明，基于ZS- dlc - paf的改进算法比距离变换、中轴线变换、形态细化和传统ZS算法的细化率最好。节点跟踪精度达到85.08%，压力测试最多可准确检测5头猪的35个节点。平均姿势匹配正确率为89.60%。本研究不仅实现了仔猪骨骼的单像素提取，而且实现了单头母猪和多头仔猪不同行为体节点之间的连接。此外，本研究建立了猪的姿势行为数据库，为研究动物行为识别分类和异常检测提供参考。关键词:仔猪，骨骼提取，姿态估计，zhangsuen, DeepLabCut，零件亲和场DOI: 10.25165/ j.j ijabe.20231603.6930引用本文:刘春青，叶海军，卢树华，唐志，白志，雕玲，等。基于ZS-DLC-PAF的仔猪骨骼提取与位姿估计。农业与生物工程学报，2023;16(3): 180 - 193。

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来源期刊

International Journal of Agricultural and Biological Engineering AGRICULTURAL ENGINEERING-

CiteScore

4.30

自引率

12.50%

发文量

审稿时长

24 weeks

期刊介绍： International Journal of Agricultural and Biological Engineering (IJABE, https://www.ijabe.org) is a peer reviewed open access international journal. IJABE, started in 2008, is a joint publication co-sponsored by US-based Association of Agricultural, Biological and Food Engineers (AOCABFE) and China-based Chinese Society of Agricultural Engineering (CSAE). The ISSN 1934-6344 and eISSN 1934-6352 numbers for both print and online IJABE have been registered in US. Now, Int. J. Agric. & Biol. Eng (IJABE) is published in both online and print version by Chinese Academy of Agricultural Engineering.