Skeleton extraction and pose estimation of piglets using ZS-DLC-PAF
Abstract
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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Nasirahmadi A, Edwards S A, Matheson S M, Sturm B. Using automated image analysis in pig behavioural research: Assessment of the influence of enrichment substrate provision on lying behaviour. Applied Animal Behaviour Science, 2017, 196: 30-35.
Naseri M, Heidari S, Gheibi R, Gong L H, Sadri A. A novel quantum binary images thinning algorithm: a quantum version of the Hilditch's algorithm. Optik-International Journal for Light and Electron Optics, 2016; 131: 678-686.
Chen C, Zhu W X, Norton T. Behaviour recognition of pigs and cattle: journey from computer vision to deep learning. Computers and Electronics in Agriculture, 2021; 187: 106255. doi: 10.1016/j.compag.2021.106255.
]Kustra J, Jalba A, Telea A. Computing refined skeletal features from medial point clouds. Pattern Recognition Letters, 2016; 76: 13-21.
Gronskyte R, Clemmensen L H, Hviid M S, Kulahci M. Pig herd monitoring and undesirable tripping and stepping prevention. Computers and Electronics in Agriculture, 2015; 119: 51-60. doi: 10.1016/j.compag.2015.09.021.
Nasirahmadi A, Hensel O, Edwards S A, Sturm B. Automatic detection of mounting behaviours among pigs using image analysis. Comput. Electronics in Agriculture, 2016; 124: 295-302.
Nasirahmadi A, Edwards S A, Sturm, B. Implementation of machine vision for detecting behaviour of cattle and pigs. Livestock Science, 2017; 202: 25-38.
Kusuma W A, Husniah L. Skeletonization using thinning method for human motion system. 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya: IEEE, 2015; pp.103-106. doi: 10.1109/ISITIA.2015.7219962
Zhang T Y, Suen C Y. A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 1984; 27(3): 236-239.
Ramya P, Rajeswari R. Human action recognition using distance transform and entropy based features. Multimedia Tools and Applications, 2021; 80(21): 8147-8173.
Shi C W, Zhao J Y, Chang J S. Skeleton feature extraction algorithm based on medial axis transformation. Computer Engineering, 2019; 45(7): 242-250. (in Chinese)
Lynda B B, Basel S, Abdelkamel T. A modified ZS thinning algorithm by a hybrid approach. The Visual Computer, 2018; 34(5): 689-706.
Lynda B B, Basel S, Abdelkamel T. Implementation and comparison of binary thinning algorithms on GPU. Computing, 2018; 101(8): 1091-1117.
Li R, Zhang X Y. Research on the improvement of EPTA parallel thinning algorithm. Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE2018), 2018; pp.994-1001. doi: 10.2991/ncce-18.2018.167
Pfister, T, Charles J, Zisserman A. Flowing convnets for human pose estimation in videos. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago: IEEE, 2015; pp.1913-1921. doi: 10.1109/ICCV.2015.222
Newell A, Huang Z A, Deng J. Associative embedding: end-to-end learning for joint detection and grouping. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017; pp.2274-2284. doi: 10.5555/3294771.3294988.
Fang H S, Xie S Q, Tai Y W, Lu C W. RMPE: Regional multi-person pose estimation. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice: IEEE, 2017; pp.2353-2362. doi: 10.1109/ICCV.2017.256.
Liao R J, Cao C S, Garcia E B, Yu S Q, Huang Y Z. Pose-based temporal-spatial network (PSTN) for gait recognition with carrying and clothing variations. In: Proceedings of the 12th Chinese Conference on Biometric Recognition (CCVR 2017), 2017; pp.474-483. doi: 10.1007/978-3-319-69923-3_51.
Cowton, J., Kyriazakis, I., Bacardit, J. Automated individual pig localisation, tracking and behaviour metric extraction using deep learning. IEEE Access, 2019; 7: 108049-108060.
Gan H M, Ou M Q, Zhao F Y, Xu C G, Li S M, Chen C X, et al. Automated piglet tracking using a single convolutional neural network. Biosystems Engineering, 2021; 205(1): 48-63.
Gan H M, Ou M Q, Huang E D, Xu C G, Li S Q, Li J P, et al. Automated detection and analysis of social behaviors among preweaning piglets using key point-based spatial and temporal features. Computers and Electronics in Agriculture, 2021; 188: 106357. doi: 10.1016/j.compag.2021.106357.
Gan H M, Li S M, Ou M Q, Yang X F, Huang B, Liu K, et al. Fast and accurate detection of lactating sow nursing behavior with CNN-based optical flow and features. Computers and Electronics in Agriculture, 2021; 189: 106384. doi: 10.1016/j.compag.2021.106384.
Mathis A, Mamidanna P, Cury K M, Abe T, Murthy V N, Mathis M W, et al. DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 2018; 21(9): 1281-1289.
Nath T, Mathis A, Chen A C, Patel A, Bethge M, Mathis M W. Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nature Protocols, 2019; 14(7): 476531. doi: 10.1101/476531.
Alameer A, Kyriazakis I, Bacardit J. Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs. Scientific Reports, 2020; 10: 13665. doi: 10.1038/s41598-020-70688-6.
Cheng F, Zhang T M, Zheng H K, Huang J D, Cuan K X. Pose estimation and behavior classifcation of broiler chickens based on deep neural networks. Computers and Electronics in Agriculture, 2021; 180: 105863. doi: 10.1016/j.compag.2020.105863
Romero-Ferrero F, Bergomi M G, Hinz R C, Heras F J H, de Polavieja G G. Idtracker.ai: Tracking all individuals in large collectives of unmarked animals. Nature Methods, 2019; 16: 179-182.
Sun S J, Akhtar N, Song H S, Mian A, Shah M. Deep affinity network for multiple object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019; 43(1): 104-119.
Zhang Y F, Wang C Y, Wang X J, Zeng W J, Liu W Y. A simple baseline for multi-object tracking. International Journal of Computer Vision, 2021; 129(11): 3069-3087.
Jiang Y Q, Wang P, Gao H W, Jin L, Liu X J. Study on the method for removing boundary burr based on relevance of chain code. In: 2011International Conference on Electronice Commerce, Web Application and Communication (ECWAC 2011), 2011; 144: 188-194. doi: 10.1007/978-3-642-20370-1_31.
Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. N: The 5th International Conference on Learning Representations, 2016. arXiv:1609.02907.
Cao Z, Simon T, Wei S E, Sheikh Y. Realtime multi-person 2D pose estimation using part affinity fields. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu: IEEE, 2017; pp.1302-1310. doi: 10.1109/CVPR.2017.143.
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