Abstract
The rapid development of multimedia information processing technology provides development opportunities for digitization in sports, among which motion capture technology, as the latest achievement of multimedia information processing technology, has gradually gained the attention of scholars and started to be used for visualization of sports movements. Therefore, this paper introduces a monocular video motion capture method and optimizes it for the problems of reconstructing human movements such as floating, ground penetration and sliding, which provides a technical path for the specific application of motion capture technology in the field of sports training and also provides a technical guarantee for the visualization of sports training movements. Introduced a new motion capture optimization method. This method captures human motion trajectories from monocular videos, and trajectory operations combine human pose estimation and physical constraints. The proposed method uses foot contact judgment to obtain foot contact events for each motion frame. Then, it optimizes the overall body motion trajectory of the key points based on the obtained contact conditions, making the generated motion visually closer to reality. This article proposes LiteHumanPose Net with a inference speed of up to 22FPS, and conducts experimental analysis and comparison of several popular pose estimation methods from the perspectives of frame rate and average accuracy, such as Sim pleBaseline, HRNet, and Hourglass Net. LiteHumanPose Net outperforms Hourglass Net in terms of frame rate and accuracy, while HRNet has high accuracy due to its multiple parameters but low frame rate. The LiteHumanPose network proposed in this article has a good balance between accuracy and frame rate, and has obvious landing advantages.
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References
Alanen AM, Räisänen AM, Benson LC, Pasanen K (2021) The use of inertial measurement units for analyzing change of direction movement in sports: a scoping review. Int J Sports Sci Coach 16(6):1332–1353
Alexanderson S, O’Sullivan C, Beskow J (2017) Real-time labeling of non-rigid motion capture marker sets. Comput Graph 69:59–67
Cai L, Liu D, Ma Y (2021) Placement recommendations for single kinect-based motion capture system in unilateral dynamic motion analysis. Healthcare 9(8):1076
Castillo S, Legde K, Cunningham DW (2018) The semantic space for motion-captured facial expressions. Comput Anim Virtual Worlds 29(3–4):e1823
Chen M, Zhou Y (2022) Analysis of students’ sports exercise behavior and health education strategy using visual perception-motion recognition algorithm. Front Psychol 13:829432
Chen Y, Xia R, Zou K, Yang K (2023) RNON: image inpainting via repair network and optimization network. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-023-01811-y
Chen Y, Xia R, Yang K, Zou K (2023) DGCA: high resolution image inpainting via DR-GAN and contextual attention. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-15313-0
Chen Y, Xia R, Yang K, Zou K (2023d) DARGS: Image inpainting algorithm via deep attention residuals group and semantics. J King Saud Univ-Comput Inform Sci 35(6):101567
Chen Y, Xia R, Yang K, Zou K (2023) MFFN: image super-resolution via multi-level features fusion network. Visual Comput, 1–16.
Diraneyya MM, Ryu J, Abdel-Rahman E, Haas CT (2021) Inertial motion capture-based whole-body inverse dynamics. Sensors 21(21):7353
Frevel N, Beiderbeck D, Schmidt SL (2022) The impact of technology on sports–a prospective study. Technol Forecast Soc Chang 182:121838
Hachaj T, Piekarczyk M, Ogiela MR (2017) Human actions analysis: templates generation, matching and visualization applied to motion capture of highly-skilled karate athletes. Sensors 17(11):2590
Holden D (2018) Robust solving of optical motion capture data by denoising. ACM Trans Graphics (TOG) 37(4):1–12
Hribernik M, Umek A, Tomažič S, Kos A (2022) Review of real-time biomechanical feedback systems in sport and rehabilitation. Sensors 22(8):3006
Ichikawa M, Masakura Y (2017) Motion capture depends upon the common fate factor among elements. Perception 46(12):1371–1385
Kredel R, Vater C, Klostermann A, Hossner E-J (2017) Eye-tracking technology and the dynamics of natural gaze behavior in sports: a systematic review of 40 years of research. Front Psychol 8:1845
Lerma N, Gulgin H (2023) Agreement of a portable motion capture system to analyze movement skills in children. Meas Phys Educ Exerc Sci 27(2):105–113
Li H, Khoo S, Yap HJ (2022) Implementation of sequence-based classification methods for motion assessment and recognition in a traditional Chinese sport (Baduanjin). Int J Environ Res Public Health 19(3):1744
Liu J, Wang L, Zhou H (2021) The application of human–computer interaction technology fused with artificial intelligence in sports moving target detection education for college athlete. Front Psychol 12:677590
Ozkaya G, Jung HR, Jeong IS, Choi MR, Shin MY, Lin X, Heo WS, Kim MS, Kim E, Lee K-K (2018) Three-dimensional motion capture data during repetitive overarm throwing practice. Scientific Data 5(1):1–6
Petri K, Lichtenstein M, Bandow N, Campe S, Wechselberger M, Sprenger D, Kaczmarek F, Emmermacher P, Witte K (2017) Analysis of anticipation by 3D motion capturing–a new method presented in karate kumite. J Sports Sci 35(2):130–135
Plaza-Bravo JM, Mateo-March M, Sanchis-Sanchis R, Pérez-Soriano P, Zabala M, Encarnación-Martínez A (2022) Validity and reliability of the Leomo motion-tracking device based on inertial measurement unit with an optoelectronic camera system for cycling pedaling evaluation. Int J Environ Res Public Health 19(14):8375
Reimer LM, Kapsecker M, Fukushima T, Jonas SM (2022) Evaluating 3D human motion capture on mobile devices. Appl Sci 12(10):4806
Smith KC, Abrams RA (2018) Motion onset really does capture attention. Atten Percept Psychophys 80:1775–1784
Sun F, Yin X (2021) Application of computer image processing technology in oilfield underground mining machinery. J Phys Conf Ser 1915(3):032051
Wang H, Weiss KJ, Haggerty MC, Heath JE (2014) The effect of active sitting on trunk motion. J Sport Health Sci 3(4):333–337
Wang Y, Liu Y, Tong X, Dai Q, Tan P (2017) Outdoor markerless motion capture with sparse handheld video cameras. IEEE Trans Visual Comput Graphics 24(5):1856–1866
Wu S, Wang Y, BolaBola JZ, Qin H, Ding W, Wen W, Niu J (2016) Incorporating motion analysis technology into modular arrangement of predetermined time standard (MODAPTS). Int J Ind Ergon 53:291–298
Yao Y, Song L, Ye J (2020) Motion-To-BMI: Using motion sensors to predict the body mass index of smartphone users. Sensors 20(4):1134
Zhao B, Liu S (2021) Basketball shooting technology based on acceleration sensor fusion motion capture technology. EURASIP J Adv Signal Process 2021(1):1–14
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Li, Y. Visualization of movements in sports training based on multimedia information processing technology. J Ambient Intell Human Comput 15, 2505–2515 (2024). https://doi.org/10.1007/s12652-024-04767-1
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DOI: https://doi.org/10.1007/s12652-024-04767-1