{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T23:29:28Z","timestamp":1740180568906,"version":"3.37.3"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T00:00:00Z","timestamp":1723334400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T00:00:00Z","timestamp":1723334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Liaoning Provincial Science and Technology Plan Project","award":["2023JH2\/101300205"],"award-info":[{"award-number":["2023JH2\/101300205"]}]},{"name":"Shenyang Science and Technology Plan Project","award":["23-407-3-33"],"award-info":[{"award-number":["23-407-3-33"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cybersecurity"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Human pose estimation is an important task in computer vision, which can provide key point detection of human body and obtain bone information. At present, human pose estimation is mainly utilized for detection of large targets, and there is no solution for detection of small targets. This paper proposes a multi-channel spatial information feature based human pose (MCSF-Pose) estimation algorithm to address the issue of medium and small targets inaccurate detection of human key points in scenarios involving occlusion and multiple poses. The MCSF-Pose network is a bottom-up regression network. Firstly, an UP-Focus module is designed to expand the feature information while reducing parameter computation during the up-sampling process. Then, the channel segmentation strategy is adopted to cut the features, and the feature information of multiple dimensions is retained through different convolutional groups, which reduces the parameter lightweight network model and makes up for the loss of the feature information associated with the depth of the network. Finally, the three-layer PANet structure is designed to reduce the complexity of the model. With the aid of the structure, it also to improve the detection accuracy and anti-interference ability of human key points. The experimental results indicate that the proposed algorithm outperforms YOLO-Pose and other human pose estimation algorithms on COCO2017 and MPII human pose datasets.<\/jats:p>","DOI":"10.1186\/s42400-024-00248-2","type":"journal-article","created":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T01:01:55Z","timestamp":1723338115000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-channel spatial information feature based human pose estimation algorithm"],"prefix":"10.1186","volume":"7","author":[{"given":"Yinghong","family":"Xie","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5067-3817","authenticated-orcid":false,"given":"Yan","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Xiaowei","family":"Han","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Biao","family":"Yin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,11]]},"reference":[{"issue":"1","key":"248_CR1","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2021","unstructured":"Cao Z, Hidalgo G, Simon T, Wei S-E, Sheikh Y (2021) OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell 43(1):172\u2013186. https:\/\/doi.org\/10.1109\/TPAMI.2019.2929257","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"248_CR2","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4194723","author":"X Chen","year":"2022","unstructured":"Chen X, Zhao Y, Qin Y et al (2022) PANet: perspective-aware network with dynamic receptive fields and self-distilling supervision for crowd counting. SSRN Electron J. https:\/\/doi.org\/10.2139\/ssrn.4194723","journal-title":"SSRN Electron J"},{"key":"248_CR3","doi-asserted-by":"publisher","unstructured":"Chen Y, Wang Z, Peng Y, et al. (2018) Cascaded pyramid network for multi-person pose estimation. In: IEEE\/CVF conference on computer vision and pattern recognition. [2023\u201310\u201307]. DOI:https:\/\/doi.org\/10.48550\/arXiv.1711.07319.","DOI":"10.48550\/arXiv.1711.07319"},{"key":"248_CR4","doi-asserted-by":"publisher","unstructured":"Cheng B, Xiao B, Wang J, et al. (2020) HigherHRNet: scale-aware representation learning for bottom-up human pose estimation. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), Seattle, WA, USA. 2020. https:\/\/doi.org\/10.1109\/cvpr42600.2020.00543.","DOI":"10.1109\/cvpr42600.2020.00543"},{"key":"248_CR5","unstructured":"Du X, Li Y, Cui Y, et al. (2021) Revisiting 3D ResNets for video recognition. Comput Vis Pattern Recogn"},{"key":"248_CR6","doi-asserted-by":"publisher","first-page":"7157","DOI":"10.1109\/TPAMI.2022.3222784","volume":"45","author":"HS Fang","year":"2022","unstructured":"Fang HS, Li J, Tang H et al (2022) Alphapose: whole-body regional multi-person pose estimation and tracking in real-time. IEEE Trans Pattern Anal Mach Intell 45:7157","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"248_CR7","doi-asserted-by":"publisher","unstructured":"Guler RA, Neverova N, Kokkinos I (2018) DensePose: dense human pose estimation in the wild. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA. https:\/\/doi.org\/10.1109\/cvpr.2018.00762","DOI":"10.1109\/cvpr.2018.00762"},{"key":"248_CR8","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/tpami.2018.2844175","volume":"2020","author":"K He","year":"2020","unstructured":"He K, Gkioxari G, Dollar P et al (2020) Mask R-CNN. IEEE Trans Pattern Anal Mach Intell 2020:386\u2013397. https:\/\/doi.org\/10.1109\/tpami.2018.2844175","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"248_CR9","unstructured":"Yinghao H, Bogo F, Lassner C (2021) Single image 3D human pose estimation via keypoint estimation and mesh convolutional neural networks"},{"key":"248_CR10","doi-asserted-by":"crossref","unstructured":"Insafutdinov E, Pishchulin L, Andres B et al. (2016) Deepercut: A deeper, stronger, and faster multi-person pose estimation model. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI 14. Springer International Publishing, pp 34\u201350","DOI":"10.1007\/978-3-319-46466-4_3"},{"issue":"1","key":"248_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1827\/1\/012138","volume":"1827","author":"Y Jiaxin","year":"2021","unstructured":"Jiaxin Y, Fang W, Jieru Y (2021) A review of action recognition based on convolutional neural network[J\/OL]. J Phys: Conf Ser 1827(1):012138. https:\/\/doi.org\/10.1088\/1742-6596\/1827\/1\/012138","journal-title":"J Phys: Conf Ser"},{"key":"248_CR12","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882","DOI":"10.3115\/v1\/D14-1181"},{"issue":"8","key":"248_CR13","doi-asserted-by":"publisher","first-page":"13498","DOI":"10.1109\/TITS.2021.3124981","volume":"23","author":"S Kreiss","year":"2021","unstructured":"Kreiss S, Bertoni L, Alahi A (2021) Openpifpaf: Composite fields for semantic keypoint detection and spatio-temporal association. IEEE Trans Intell Transp Syst 23(8):13498\u201313511","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"248_CR14","doi-asserted-by":"publisher","unstructured":"Lin TY, Dollar P, Girshick R, et al. (2017) Feature pyramid networks for object detection. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI. https:\/\/ieeexplore.ieee.org\/document\/8099589\/. DOI:https:\/\/doi.org\/10.1109\/cvpr.2017.106.","DOI":"10.1109\/cvpr.2017.106"},{"issue":"4","key":"248_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3524497","volume":"55","author":"W Liu","year":"2023","unstructured":"Liu W, Qian Bao Y, Sun TM (2023) Recent advances of monocular 2D and 3D human pose estimation: a deep learning perspective. ACM Comput Surv 55(4):1\u201341. https:\/\/doi.org\/10.1145\/3524497","journal-title":"ACM Comput Surv"},{"key":"248_CR16","doi-asserted-by":"publisher","unstructured":"Liu S, Qi L, Qin H, et al. (2018) Path Aggregation network for instance segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT. https:\/\/doi.org\/10.1109\/cvpr.2018.00913.","DOI":"10.1109\/cvpr.2018.00913"},{"key":"248_CR17","doi-asserted-by":"publisher","unstructured":"Liu W, Ren G, Yu R, et al. (2022) Image-Adaptive YOLO for object detection in adverse weather conditions. In: Proceedings of the AAAI conference on artificial intelligence, pp 1792\u20131800. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/20072. DOI:https:\/\/doi.org\/10.1609\/aaai.v36i2.20072.","DOI":"10.1609\/aaai.v36i2.20072"},{"key":"248_CR18","doi-asserted-by":"publisher","unstructured":"Maji D, Nagori S, Mathew M, et al. (2022) YOLO-Pose: enhancing YOLO for multi person pose estimation using object keypoint similarity loss. DOI:https:\/\/doi.org\/10.48550\/arXiv.2204.06806.","DOI":"10.48550\/arXiv.2204.06806"},{"key":"248_CR19","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1177\/14759217221089571","volume":"2023","author":"JC Ong","year":"2023","unstructured":"Ong JC, Lau SL, Ismadi MZ et al (2023) Feature pyramid network with self-guided attention refinement module for crack segmentation. Struct Health Monitor 2023:672\u2013688. https:\/\/doi.org\/10.1177\/14759217221089571","journal-title":"Struct Health Monitor"},{"key":"248_CR20","doi-asserted-by":"publisher","unstructured":"Papandreou G, Zhu T, Chen L C, et al. (2018) PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model. In: European conference on computer vision. Springer, Cham, DOI:https:\/\/doi.org\/10.1007\/978-3-030-01264-9_17.","DOI":"10.1007\/978-3-030-01264-9_17"},{"key":"248_CR21","doi-asserted-by":"publisher","first-page":"106780","DOI":"10.1016\/j.compag.2022.106780","volume":"2022","author":"J Qi","year":"2022","unstructured":"Qi J, Liu X, Liu K et al (2022) An improved YOLOv5 model based on visual attention mechanism: application to recognition of tomato virus disease. Comput Electron Agric 2022:106780. https:\/\/doi.org\/10.1016\/j.compag.2022.106780","journal-title":"Comput Electron Agric"},{"key":"248_CR22","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, et al. (2016) You only look once: unified, real-time object detection. In: Computer Vision & Pattern Recognition. IEEE, DOI:https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"248_CR23","doi-asserted-by":"publisher","unstructured":"Sun K, Xiao B, Liu D, et al. Deep high-resolution representation learning for human pose estimation. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), Long Beach, CA, USA. 2019. https:\/\/doi.org\/10.1109\/cvpr.2019.00584.","DOI":"10.1109\/cvpr.2019.00584"},{"key":"248_CR24","doi-asserted-by":"crossref","unstructured":"Xiao B, Wu H, Wei Y (2018) Simple baselines for human pose estimation and tracking. In: Proc. of the European CONF. ON COMPUTER VISIOn (ECCV). pp 466\u2013481.","DOI":"10.1007\/978-3-030-01231-1_29"},{"issue":"01","key":"248_CR25","doi-asserted-by":"publisher","first-page":"9038","DOI":"10.1609\/aaai.v33i01.33019038","volume":"33","author":"E Xie","year":"2019","unstructured":"Xie E, Zang Y, Shao S, Gang Y, Yao C, Li G (2019) Scene text detection with supervised pyramid context network. Proc AAAI Conf AI 33(01):9038\u20139045. https:\/\/doi.org\/10.1609\/aaai.v33i01.33019038","journal-title":"Proc AAAI Conf AI"},{"key":"248_CR26","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.3390\/rs13091619","volume":"2021","author":"B Yan","year":"2021","unstructured":"Yan B, Fan P, Lei X et al (2021) A real-time apple targets detection method for picking robot based on improved YOLOv5. Remote Sens 2021:1619. https:\/\/doi.org\/10.3390\/rs13091619","journal-title":"Remote Sens"},{"key":"248_CR27","doi-asserted-by":"publisher","unstructured":"Yang G, Feng W, Jin J, et al. (2020) Face mask recognition system with YOLOV5 based on image recognition. In: 2020 IEEE 6th international conference on computer and communications (ICCC), Chengdu, China. 2020. https:\/\/doi.org\/10.1109\/iccc51575.2020.9345042.","DOI":"10.1109\/iccc51575.2020.9345042"}],"container-title":["Cybersecurity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-024-00248-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42400-024-00248-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-024-00248-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T01:02:17Z","timestamp":1723338137000},"score":1,"resource":{"primary":{"URL":"https:\/\/cybersecurity.springeropen.com\/articles\/10.1186\/s42400-024-00248-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,11]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["248"],"URL":"https:\/\/doi.org\/10.1186\/s42400-024-00248-2","relation":{},"ISSN":["2523-3246"],"issn-type":[{"type":"electronic","value":"2523-3246"}],"subject":[],"published":{"date-parts":[[2024,8,11]]},"assertion":[{"value":"3 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"49"}}