{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T23:01:09Z","timestamp":1781218869594,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T00:00:00Z","timestamp":1640563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The human body generates infrared radiation through the thermal movement of molecules. Based on this phenomenon, infrared images of the human body are often used for monitoring and tracking. Among them, key point location on infrared images of the human body is an important technology in medical infrared image processing. However, the fuzzy edges, poor detail resolution, and uneven brightness distribution of the infrared image of the human body cause great difficulties in positioning. Therefore, how to improve the positioning accuracy of key points in human infrared images has become the main research direction. In this study, a multi-scale convolution fusion deep residual network (Mscf-ResNet) model is proposed for human body infrared image positioning. This model is based on the traditional ResNet, changing the single-scale convolution to multi-scale and fusing the information of different receptive fields, so that the extracted features are more abundant and the degradation problem, caused by the excessively deep network, is avoided. The experiments show that our proposed method has higher key point positioning accuracy than other methods. At the same time, because the network structure of this paper is too deep, there are too many parameters and a large volume of calculations. Therefore, a more lightweight network model is the direction of future research.<\/jats:p>","DOI":"10.3390\/fi14010015","type":"journal-article","created":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T01:20:43Z","timestamp":1640654443000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Key Points\u2019 Location in Infrared Images of the Human Body Based on Mscf-ResNet"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2030-1479","authenticated-orcid":false,"given":"Shengguo","family":"Ge","sequence":"first","affiliation":[{"name":"Faculty of Computer Science & Information Technology, University Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7818-8208","authenticated-orcid":false,"given":"Siti Nurulain Mohd","family":"Rum","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science & Information Technology, University Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,27]]},"reference":[{"key":"ref_1","first-page":"3","article-title":"Infrared imaging system and applications","volume":"44","author":"Li","year":"2014","journal-title":"Laser Infrared"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"76993","DOI":"10.1109\/ACCESS.2018.2883988","article-title":"Induction motor failure analysis: An automatic methodology based on infrared imaging","volume":"6","year":"2018","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1038\/s41551-020-0569-y","article-title":"Non-invasive monitoring of chronic liver disease via near-infrared and shortwave-infrared imaging of endogenous lipofuscin","volume":"4","author":"Saif","year":"2020","journal-title":"Nat. Biomed. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e20089","DOI":"10.1002\/vzj2.20089","article-title":"The feasibility of shortwave infrared imaging and inverse numerical modeling for rapid estimation of soil hydraulic properties","volume":"20","author":"Babaeian","year":"2021","journal-title":"Vadose Zone J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"033106","DOI":"10.1117\/1.OE.59.3.033106","article-title":"Analysis and modeling of observer performance while using an infrared imaging system","volume":"59","author":"Hixson","year":"2020","journal-title":"Opt. Eng."},{"key":"ref_6","first-page":"1027","article-title":"Application of thermal infrared technology in traditional Chinese medicine diagnosis","volume":"13","author":"Qinyuan","year":"2011","journal-title":"World Sci. Technol. Mod. Tradit. Chin. Med. Mater. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-018-1140-1","article-title":"Infrared thermal imaging for diabetes detection and measurement","volume":"43","author":"Selvarani","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zou, T., Chen, G., Li, Z., He, W., Qu, S., Gu, S., and Knoll, A. (2021). KAM-Net: Keypoint-Aware and Keypoint-Matching Network for Vehicle Detection from 2D Point Cloud. IEEE Trans. Artif. Intell.","DOI":"10.1109\/TAI.2021.3112945"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, Y., Sun, W., Huang, H., Liu, J., Fan, H., and Sun, J. (2020, January 13\u201319). Pvn3d: A deep point-wise 3d keypoints voting network for 6dof pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01165"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Mori, G. (2008). Multiple tree models for occlusion and spatial constraints in human pose estimation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-540-88690-7_53"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, F., and Li, Y. (2013, January 23\u201328). Beyond physical connections: Tree models in human pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.83"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dantone, M., Gall, J., Leistner, C., and Van Gool, L. (2013, January 23\u201328). Human pose estimation using body parts dependent joint regressors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.391"},{"key":"ref_13","unstructured":"Sun, M., Kohli, P., and Shotton, J. (2012, January 16\u201321). Conditional regression forests for human pose estimation. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kiefel, M., and Gehler, P.V. (2014). Human pose estimation with fields of parts. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10602-1_22"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hara, K., and Chellappa, R. (2013, January 23\u201328). Computationally efficient regression on a dependency graph for human pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.435"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Toshev, A., and Szegedy, C.D. (2014). Human Pose Estimation via Deep Neural Networks\u2019, CVPR.","DOI":"10.1109\/CVPR.2014.214"},{"key":"ref_17","first-page":"1799","article-title":"Joint training of a convolutional network and a graphical model for human pose estimation","volume":"27","author":"Tompson","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pfister, T., Charles, J., and Zisserman, A. (2015, January 7\u201313). Flowing convnets for human pose estimation in videos. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.222"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wei, S.-E., Ramakrishna, V., Kanade, T., and Sheikh, Y. (2016, January 27\u201330). Convolutional pose machines. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.511"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., and Deng, J. (2016). Stacked hourglass networks for human pose estimation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, W., Li, S., Ouyang, W., Li, H., and Wang, X. (2017, January 22\u201329). Learning feature pyramids for human pose estimation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.144"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Carreira, J., Agrawal, P., Fragkiadaki, K., and Malik, J. (2016, January 27\u201330). Human pose estimation with iterative error feedback. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.512"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lifshitz, I., Fetaya, E., and Ullman, S. (2016). Human pose estimation using deep consensus voting. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46475-6_16"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P.V., and Schiele, B. (2016, January 27\u201330). Deepcut: Joint subset partition and labeling for multi person pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.533"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., and Schiele, B. (2016). Deepercut: A deeper, stronger, and faster multi-person pose estimation model. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46466-4_3"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xia, F., Wang, P., Chen, X., and Yuille, A.L. (2017, January 21\u201326). Joint multi-person pose estimation and semantic part segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.644"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.-E., and Sheikh, Y. (2017, January 21\u201326). Realtime multi-person 2d pose estimation using part affinity fields. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Zhu, T., Kanazawa, N., Toshev, A., Tompson, J., Bregler, C., and Murphy, K. (2017, January 21\u201326). Towards accurate multi-person pose estimation in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.395"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., and Sun, J. (2018, January 18\u201323). Cascaded pyramid network for multi-person pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00742"},{"key":"ref_33","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2014, January 23\u201328). Learning and transferring mid-level image representations using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.222"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rojas, R. (1996). The backpropagation algorithm. Neural Networks, Springer.","DOI":"10.1007\/978-3-642-61068-4"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Murray, N., and Perronnin, F. (2014, January 23\u201328). Generalized max pooling. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.317"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, X., Irie, G., Hiramatsu, K., and Kashino, K. (2018, January 7\u201310). Weighted generalized mean pooling for deep image retrieval. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451317"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Vinyals, O., Senior, A., and Sak, H. (2015, January 19\u201324). Convolutional, long short-term memory, fully connected deep neural networks. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Australia.","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yan, C., and Wang, Y. (2009, January 11\u201313). A Novel Multi-User Face Detection under Infrared Illumination by Real Adaboost. Proceedings of the 2009 International Conference on Computational Intelligence and Software Engineering, Wuhan, China.","DOI":"10.1109\/CISE.2009.5366152"},{"key":"ref_42","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning, PMLR."},{"key":"ref_43","unstructured":"Mish, M.D. (2019). A self regularized non-monotonic neural activation function. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/1\/15\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:54:23Z","timestamp":1760169263000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/1\/15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,27]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["fi14010015"],"URL":"https:\/\/doi.org\/10.3390\/fi14010015","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,27]]}}}