Giant panda pose estimation method based on high resolution net
Received date: 2021-11-17
Accepted date: 2022-03-14
Online published: 2022-07-22
Long-term behavioral monitoring of captive giant pandas (Ailuropoda melanoleuca) can help animal managers better understand the panda’s physiological cycle and health status in a timely manner, and help breeding facilities quickly take corresponding husbandry actions to improve breeding management. At present, neither animal managers nor scientists can monitor giant pandas 24 hours a day and obtain corresponding behavioral information on time. Accurate animal pose estimation is an important factor in animal behavior research and is also the basis for many downstream applications. Understanding the pose of giant pandas can greatly promote the research of panda behavior and improve its conservation and management. In order to improve the accuracy of giant panda pose estimation in complex environments, this paper proposed a pose estimation method based on the high-resolution network HRNet-32. To solve the problem of large-scale differences in different parts of the giant pandas, an atrous spatial pyramid pooling module was introduced in HRNet-32, which used dilated convolution with different dilated rates to form a similar pyramid form, so as to capture multi-scale information while enhancing the feature’s receptive field. Meanwhile, the giant panda pose estimation was regarded as a homogeneous multi-task learning problem, the joint points of the giant panda were grouped, and the part-based multi-branch structure was introduced to learn the representations specific to each part group. The results of several comparison experiments show that the model proposed in this paper, PCK@0.05, had a high detection accuracy (81.51%). The method proposed in this paper can provide technical support for the behavioral analysis and health assessment of giant pandas.
Key words： Giant panda; Posture estimation; Image analysis; Deep learning
Yu QI , Han SU , Rong HOU , Peng LIU , Peng CHEN , Hangxing ZANG , Zhihe ZHANG . Giant panda pose estimation method based on high resolution net[J]. ACTA THERIOLOGICA SINICA, 2022 , 42(4) : 451 -460 . DOI: 10.16829/j.slxb.150639
| null | Cao J K, Tang H Y, Fang H S, Shen X Y, Tai Y W, Lu C W. 2019. Cross-domain adaptation for animal pose estimation. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). DOI:10.1109/iccv.2019.00959 . |
| null | Cao Z, Simon T, Wei S E, Sheikh Y. 2017. Realtime multi-person 2d pose estimation using part affinity fields. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/cvpr.2017.143 . |
| null | Caruana R. 1997. Multitask learning. Machine Learning, 28 (1): 41-75. |
| null | Chen L C, Papandreou G, Schroff F, Adam H. 2017. Rethinking atrous convolution for semantic Image segmentation. arXiv preprint arXiv: . |
| null | Chen X L, Zhu Y, Li Y D, Zhang G Q. 2016. The influence of human activities on the behavior of captive giant panda in summer at Mount Emei. Sichuan Journal of Zoology, 35 (5): 680-685. (in Chinese) |
| null | Chen Y K, Song Y N, He J J, Xu R H, Huang X B. 2019. Animal pose estimation and state assessment based on deep learning. Electronics World, (5): 47-48. (in Chinese) |
| null | Chen Y L, Wang Z C, Peng Y X, Zhang Z Q, Yu G, Sun J. 2018. Cascaded pyramid network for multi-person pose estimation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. DOI: 10.1109/cvpr.2018.00742 . |
| null | Fang H S, Xie S Q, Tai Y W, Lu C W. 2017. Rmpe: Regional multi-person pose estimation. 2017 IEEE International Conference on Computer Vision (ICCV). DOI: 10.1109/iccv.2017.256 . |
| null | Feng L Q, Zhao Y Q, Sun Y C, Zhao W X, Tang J X. 2021. Action recognition using a spatial-temporal network for wild felines. Animals, 11 (2): 485. |
| null | He K M, Zhang X Y, Ren S Q, Sun J. 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/cvpr.2016.90 . |
| null | He Q, Zhao Q J, Liu N, Chen P, Zhang Z H, Hou R. 2019. Distinguishing individual red pandas from their faces. Chinese Conference on Pattern Recognition and Computer Vision (PRCV). DOI: 10.1007/978-3-030-31723-2_61 . |
| null | Hu P Y, Ramanan D. 2016. Bottom-up and top-down reasoning with hierarchical rectified gaussians. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/cvpr.2016.604 . |
| null | Koolhaas J M, Van Reenen C G. 2016. Animal behavior and well-being symposium: Interaction between coping style/personality, stress, and welfare: Relevance for domestic farm animals. Journal of Animal Science, 94 (6): 2284-2296. |
| null | Li C W, Wang C D, Yan B Y, Chen S J, Wu H L, Huang X Y, Jin S Y, Huang S, Chen L, Li D S. 2012. The determination of blood physiological indices for the giant panda. Acta Theriologica Sinica, 32 (3): 266-270. (in Chinese) |
| null | Li C, Lee G H. 2021. From synthetic to real: Unsupervised domain adaptation for animal pose estimation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. |
| null | Li X Y, Cai C, Zhang R F, Ju L, He J R. 2019. Deep cascaded convolutional models for cattle pose estimation. Computers and Electronics in Agriculture, 164: 104885. |
| null | Lin T Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick C L. 2014. Microsoft coco: Common objects in context. European Conference on Computer Vision. |
| null | Liu J, Chen Y, Guo L R, Gu B, Liu H, Hou A Y, Liu X F, Sun L X, Liu D Z. 2006. Stereotypic behavior and fecal cortisol level in captive giant pandas in relation to environmental enrichment. Zoo Biology, 25 (6): 445-459. |
| null | Liu N, Zhao Q J, Zhang N, Cheng X H, Zhu J N. 2019. Pose?guided complementary features learning for amur tiger re-identification. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops. |
| null | Mu J T, Qiu W C, Hager G D, Yuille A L. 2020. Learning from synthetic animals. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/cvpr42600.2020.01240 . |
| null | Newell A, Yang K Y, Deng J. 2016. Stacked hourglass networks for human pose estimation. European Conference on Computer Vision. DOI: 10.1007/978-3-319-46484-8_29 . |
| null | Olivas E S, Guerrero J D M, Martinez-Sober M, Magdalena-Benedito J R, Serrano L, eds. 2019. Handbook of research on machine learning applications and trends: Algorithms, methods, and techniques: Algorithms, methods, and techniques. IGI Global. |
| null | Pishchulin L, Andriluka M, Gehler P, Schiele B. 2013. Poselet conditioned pictorial structures. 2013 IEEE Conference on Computer Vision and Pattern Recognition. DOI: 10.1109/cvpr.2013.82 . |
| null | Pishchulin L, Insafutdinov E, Tang S Y, Andres B, Andriluka M, Gehler P, Schiele B. 2016. Deepcut: Joint subset partition and labeling for multi person pose estimation. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/cvpr.2016.533 . |
| null | Ruder S. 2017. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv: . |
| null | Rushen J, Butterworth A, Swanson J C. 2011. Animal behavior and well-being symposium: Farm animal welfare assurance: Science and application. Journal of Animal Science, 89 (4): 1219-1228. |
| null | Schofield D, Nagrani A, Zisserman A, Hayashi M, Matsuzawa T, Biro D, Carvalho S. 2019. Chimpanzee face recognition from videos in the wild using deep learning. Science Advances, 5 (9): eaaw0736. |
| null | Sun K, Xiao B, Liu D, Wang J D. 2019. Deep high-resolution representation learning for human pose estimation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/cvpr.2019.00584 . |
| null | Tang W, Wu Y. 2019. Does learning specific features for related parts help human pose estimation? 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/cvpr.2019.00120 . |
| null | Tian Y D, Zitnick C L, Narasimhan S G. 2012. Exploring the spatial hierarchy of mixture models for human pose estimation. European Conference on Computer Vision. DOI: 10.1007/978-3-642-33715-4_19 . |
| null | Toshev A, Szegedy C. 2014. Deeppose: Human pose estimation via deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. |
| null | Wang H N, Su H, Chen P, Hou R, Zhang Z H, Xie W Y. 2019. Learning deep features for giant panda gender classification using face images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops. |
| null | Xiao B, Wu H P, Wei Y C. 2018. Simple baselines for human pose estimation and tracking. Proceedings of the European Conference on Computer Vision (ECCV). DOI: 10.1007/978-3-030-01231-1_29 . |
| null | Yang W, Li S, Ouyang W L, Li H S, Wang X G. 2017. Learning feature pyramids for human pose estimation. Proceedings of the IEEE International Conference on Computer Vision. DOI: 10.1109/iccv.2017.144 . |
| null | Yang Y, Jia J B, Zhao Y J. 2019. Effects of human factors on panda behavior in captivity. Journal of Northeast Forestry University, 47 (12): 104-106. (in Chinese) |
| null | Yu H, Xu Y F, Zhang J, Zhao W, Guan Z Y, Tao D C. 2021. AP- 10K: A benchmark for animal pose estimation in the wild. arXiv preprint arXiv: . |
| null | Yue L H, Li J X, Liu Q S. 2021. Body parts relevance learning via expectation–maximization for human pose estimation. Multimedia Systems, 27 (5): 927-939. |
| null | Zhang B W, Li M, Zhang Z J, Goossens B, Zhu L F, Zhang S N, Hu J C, Bruford M W, Wei F W. 2007. Genetic viability and population history of the giant panda, putting an end to the‘Evolutionary Dead End’? Molecular Biology and Evolution, 24 (8): 1801-1810. |
| null | Zhang F Y, Wang M L, Wang Z C. 2021. Construction of the animal skeletons keypoint detection model based on transformer and scale fusion. Transactions of the Chinese Society of Agricultural Engineering, 37 (23): 179-185. (in Chinese) |
| null | Zhou F X, Jiang Z H, Liu Z H, Chen F, Chen L, Tong L, Yang Z L, Wang H K, Fei M R, Li L, Zhou H Y. 2021. Structured context enhancement network for mouse pose estimation. IEEE Transactions on Circuits and Systems for Video Technology. DOI:10.1109/tcsvt.2021.3098497 . |
| null | Zhou J L, Ma G Q, Li Q S, Liu C H, Liu D D, Ye Y L, Wang X B. 2012. Exploitation on spring stereotypic behavior of giant pandas in wildlife park of high?altitude region. Sichuan Journal of Zoology, 31 (3): 342-347. (in Chinese) |
| null | Zhou X, Hu H P, Li T, Deng L H, Li R G. 2013. Behavior and conservation of giant panda. Green Technology, (2): 33-36. (in Chinese) |
| null | 李才武, 王承东, 严玉宝, 陈世界, 吴虹林, 黄晓宇, 金森燕, 黄山, 陈亮, 李德生. 2012. 大熊猫血液生理指标的测定. 兽类学报, 32 (3): 266-270. |
| null | 李凯年. 2012. 饲养人员行为对农场动物福利与生产性能的影响. 中国动物保健, 14 (1): 4-8. |
| null | 杨勇, 贾竞波, 赵英杰. 2019. 圈养环境中人为因素对大熊猫行为的影响. 东北林业大学学报, 47 (12): 104-106. |
| null | 陈永康, 宋亚男, 何嘉俊, 徐荣华, 黄栩滨. 2019. 基于深度学习的动物姿态估计和状态评估研究. 电子世界, (5): 47-48. |
| null | 陈艳, 王永峰, 魏荣平, 邓林华. 2019. 圈养大熊猫应激的研究进展. 畜牧兽医科技信息, (2): 15-17. |
| null | 陈绪玲, 朱英, 李裕冬, 张贵权. 2016. 人为因素对峨眉山圈养大熊猫夏季行为的影响. 四川动物, 35 (5): 680-685. |
| null | 张飞宇, 王美丽, 王正超. 2021. 引入Transformer和尺度融合的动物骨骼关键点检测模型构建. 农业工程学报, 37 (23): 179-185. |
| null | 周杰珑, 马国强, 李奇生, 刘成会, 刘丹丹, 叶研琳, 王雄波. 2012. 高海拔地区昆明野生动物园大熊猫春季刻板行为探究. 四川动物, 31 (3): 342-347. |
| null | 周晓, 胡海平, 李倜, 邓林华, 李仁贵. 2013. 大熊猫的行为和保护研究. 绿色科技, (2): 33-36. |
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