{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:45:37Z","timestamp":1782949537884,"version":"3.54.5"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031936876","type":"print"},{"value":"9783031936883","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-031-93688-3_19","type":"book-chapter","created":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T08:07:19Z","timestamp":1752912439000},"page":"264-278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Hierarchical Pipeline for\u00a0Fine-Grained Punch Recognition in\u00a0Uncontrolled Setting"],"prefix":"10.1007","author":[{"given":"Vipul","family":"Baghel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sagar Deep","family":"Deb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rithihas","family":"Nagisetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babji","family":"Srinivasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi","family":"Hegde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"issue":"2","key":"19_CR1","first-page":"187","volume":"16","author":"HA Abdul-Azim","year":"2015","unstructured":"Abdul-Azim, H.A., Hemayed, E.E.: Human action recognition using trajectory-based representation. Egypt. Inf. J. 16(2), 187\u2013198 (2015)","journal-title":"Egypt. Inf. J."},{"issue":"17","key":"19_CR2","doi-asserted-by":"publisher","first-page":"6463","DOI":"10.3390\/s22176463","volume":"22","author":"MH Arshad","year":"2022","unstructured":"Arshad, M.H., Bilal, M., Gani, A.: Human activity recognition: review, taxonomy and open challenges. Sensors 22(17), 6463 (2022)","journal-title":"Sensors"},{"issue":"4","key":"19_CR3","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.3390\/s22041347","volume":"22","author":"R Cuperman","year":"2022","unstructured":"Cuperman, R., Jansen, K.M., Ciszewski, M.G.: An end-to-end deep learning pipeline for football activity recognition based on wearable acceleration sensors. Sensors 22(4), 1347 (2022)","journal-title":"Sensors"},{"key":"19_CR4","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/s10489-020-01823-z","volume":"51","author":"O Elharrouss","year":"2021","unstructured":"Elharrouss, O., Almaadeed, N., Al-Maadeed, S., Bouridane, A., Beghdadi, A.: A combined multiple action recognition and summarization for surveillance video sequences. Appl. Intell. 51, 690\u2013712 (2021)","journal-title":"Appl. Intell."},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Fang, H.S., et al.: Alphapose: whole-body regional multi-person pose estimation and tracking in real-time. IEEE Trans. Pattern Anal. Mach. Intell. (2022)","DOI":"10.1109\/TPAMI.2022.3222784"},{"key":"19_CR6","unstructured":"Gatt, I.: Effects of bandaging techniques and shot types on wrist motion in boxing. Ph.D. thesis, Sheffield Hallam University (2023)"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Giannakeris, P., et al.: Fusion of multimodal sensor data for effective human action recognition in the service of medical platforms. In: International Conference on Multimedia Modeling, pp. 367\u2013378. Springer (2021)","DOI":"10.1007\/978-3-030-67835-7_31"},{"issue":"4","key":"19_CR8","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1109\/TSMC.2016.2617465","volume":"47","author":"Y Guo","year":"2016","unstructured":"Guo, Y., Tao, D., Liu, W., Cheng, J.: Multiview cauchy estimator feature embedding for depth and inertial sensor-based human action recognition. IEEE Trans. Syst. Man Cybern. Syst. 47(4), 617\u2013627 (2016)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"issue":"2","key":"19_CR9","first-page":"100046","volume":"1","author":"S Gupta","year":"2021","unstructured":"Gupta, S.: Deep learning based human activity recognition (har) using wearable sensor data. Int. J. Inf. Manag. Data Insights 1(2), 100046 (2021)","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Hudovernik, V., Skocaj, D.: Video-based detection of combat positions and automatic scoring in jiu-jitsu. In: Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports, pp. 55\u201363 (2022)","DOI":"10.1145\/3552437.3555707"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Jayakumar, B., Govindarajan, N.: Multi-sensor fusion based optimized deep convolutional neural network for boxing punch activity recognition. In: Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, p. 17543371241237085 (2024)","DOI":"10.1177\/17543371241237085"},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.cviu.2017.04.007","volume":"159","author":"S Kasiri","year":"2017","unstructured":"Kasiri, S., Fookes, C., Sridharan, S., Morgan, S.: Fine-grained action recognition of boxing punches from depth imagery. Comput. Vis. Image Underst. 159, 143\u2013153 (2017)","journal-title":"Comput. Vis. Image Underst."},{"issue":"5","key":"19_CR13","doi-asserted-by":"publisher","first-page":"14885","DOI":"10.1007\/s11042-020-08806-9","volume":"83","author":"MA Khan","year":"2024","unstructured":"Khan, M.A., et al.: Human action recognition using fusion of multiview and deep features: an application to video surveillance. Multimedia Tools Appl. 83(5), 14885\u201314911 (2024)","journal-title":"Multimedia Tools Appl."},{"issue":"3","key":"19_CR14","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.3390\/app11031223","volume":"11","author":"I Khasanshin","year":"2021","unstructured":"Khasanshin, I.: Application of an artificial neural network to automate the measurement of kinematic characteristics of punches in boxing. Appl. Sci. 11(3), 1223 (2021)","journal-title":"Appl. Sci."},{"issue":"6","key":"19_CR15","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"138106","DOI":"10.1109\/ACCESS.2021.3118038","volume":"9","author":"A Labintsev","year":"2021","unstructured":"Labintsev, A., Khasanshin, I., Balashov, D., Bocharov, M., Bublikov, K.: Recognition punches in karate using acceleration sensors and convolution neural networks. IEEE Access 9, 138106\u2013138119 (2021)","journal-title":"IEEE Access"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, L., He, R., Wang, Z., Wu, G., Wang, L.: Multisports: a multi-person video dataset of spatio-temporally localized sports actions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13536\u201313545 (2021)","DOI":"10.1109\/ICCV48922.2021.01328"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Luo, Y., et al.: Lstm pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5207\u20135215 (2018)","DOI":"10.1109\/CVPR.2018.00546"},{"key":"19_CR19","unstructured":"Makarov, I., Petrov, S.: On the impact of computer vision algorithms on sport training automation: proof of concept for shadow boxing virtual instructor (2021)"},{"issue":"14","key":"19_CR20","doi-asserted-by":"publisher","first-page":"6848","DOI":"10.3390\/app12146848","volume":"12","author":"AA Malibari","year":"2022","unstructured":"Malibari, A.A., et al.: Quantum water strider algorithm with hybrid-deep-learning-based activity recognition for human-computer interaction. Appl. Sci. 12(14), 6848 (2022)","journal-title":"Appl. Sci."},{"issue":"5","key":"19_CR21","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.3390\/s21051636","volume":"21","author":"S Mekruksavanich","year":"2021","unstructured":"Mekruksavanich, S., Jitpattanakul, A.: Lstm networks using smartphone data for sensor-based human activity recognition in smart homes. Sensors 21(5), 1636 (2021)","journal-title":"Sensors"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Mohith, S.S., Vijay, S., Sanjana, V., Krupa, N.: Trajectory based human action recognition using centre symmetric local binary pattern descriptors. In: 2020 IEEE 17th India Council International Conference (INDICON), pp.\u00a01\u20136. IEEE (2020)","DOI":"10.1109\/INDICON49873.2020.9342248"},{"key":"19_CR23","unstructured":"Ni, J., Tang, H., Ngu, A.H., Liu, G., Yan, Y.: Physical-aware cross-modal adversarial network for wearable sensor-based human action recognition. arXiv preprint arXiv:2307.03638 (2023)"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"dos S\u00a0Silva, F.H., et al.: A novel feature extractor for human action recognition in visual question answering. Pattern Recogn. Lett. 147, 41\u201347 (2021)","DOI":"10.1016\/j.patrec.2021.04.002"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Shujah Islam, M.: Computer vision-based approach for skeleton-based action recognition, sahc, pp. 1\u201312. Signal, Image and Video Processing (2023)","DOI":"10.1007\/s11760-023-02829-z"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Stefa\u0144ski, P., Jach, T., Kozak, J.: Classification of punches in olympic boxing using static rgb cameras. In: International Conference on Computational Collective Intelligence, pp. 540\u2013551. Springer (2023)","DOI":"10.1007\/978-3-031-41456-5_41"},{"issue":"9","key":"19_CR27","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.3390\/s21093071","volume":"21","author":"M Stoeve","year":"2021","unstructured":"Stoeve, M., Schuldhaus, D., Gamp, A., Zwick, C., Eskofier, B.M.: From the laboratory to the field: Imu-based shot and pass detection in football training and game scenarios using deep learning. Sensors 21(9), 3071 (2021)","journal-title":"Sensors"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Wang, X., Guo, Y.: The deep learning-based human action recognition system for competitive sports. J. Imaging Sci. Technol. 1\u201316 (2024)","DOI":"10.2352\/J.ImagingSci.Technol.2024.68.3.030405"},{"issue":"2","key":"19_CR29","doi-asserted-by":"publisher","first-page":"360","DOI":"10.3390\/iot1020021","volume":"1","author":"MT Worsey","year":"2020","unstructured":"Worsey, M.T., Espinosa, H.G., Shepherd, J.B., Thiel, D.V.: An evaluation of wearable inertial sensor configuration and supervised machine learning models for automatic punch classification in boxing. IoT 1(2), 360\u2013381 (2020)","journal-title":"IoT"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Wu, F., et al.: A survey on video action recognition in sports: datasets, methods and applications. IEEE Trans. Multimedia (2022)","DOI":"10.1109\/TMM.2022.3232034"},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Yang, Q., Xia, S.: Boxnet: fine-grained action classification in boxing. In: 2023 35th Chinese Control and Decision Conference (CCDC), pp. 3520\u20133525. IEEE (2023)","DOI":"10.1109\/CCDC58219.2023.10327379"},{"key":"19_CR32","doi-asserted-by":"publisher","first-page":"115640","DOI":"10.1016\/j.image.2019.115640","volume":"80","author":"Y Yi","year":"2020","unstructured":"Yi, Y., Li, A., Zhou, X.: Human action recognition based on action relevance weighted encoding. Sig. Process. Image Commun. 80, 115640 (2020)","journal-title":"Sig. Process. Image Commun."},{"key":"19_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, Q.: A human action recognition algorithm in dynamic scene of emergency rescue. In: 2021 IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET), pp. 12\u201316. IEEE (2021)","DOI":"10.1109\/CCET52649.2021.9544460"},{"key":"19_CR34","doi-asserted-by":"crossref","unstructured":"Zhu, W., Ma, X., Liu, Z., Liu, L., Wu, W., Wang, Y.: Motionbert: a unified perspective on learning human motion representations. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15085\u201315099 (2023)","DOI":"10.1109\/ICCV51070.2023.01385"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-93688-3_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:15:21Z","timestamp":1782947721000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-93688-3_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"ISBN":["9783031936876","9783031936883"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-93688-3_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"20 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chennai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cvip2024.iiitdm.ac.in\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}