Motion Capture for Sporting Events Based on Graph Convolutional Neural Networks and Single Target Pose Estimation Algorithms
Source abstract
Abstract
Human pose estimation refers to accurately estimating the position of the human body from a single RGB image and detecting the location of the body. It serves as the basis for several computer vision tasks, such as human tracking, 3D reconstruction, and autonomous driving. Improving the accuracy of pose estimation has significant implications for the advancement of computer vision. This paper addresses the limitations of single-branch networks in pose estimation. It presents a top-down single-target pose estimation approach based on multi-branch self-calibrating networks combined with graph convolutional neural networks. The study focuses on two aspects: human body detection and human body pose estimation. The human body detection is for athletes appearing in sports competitions, followed by human body pose estimation, which is divided into two methods: coordinate regression-based and heatmap test-based. To improve the accuracy of the heatmap test, the high-resolution feature map output from HRNet is used for deconvolution to improve the accuracy of single-target pose estimation recognition.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human Pose and Action Recognition · Video Surveillance and Tracking Methods · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Xi’an Institute of Optics and Precision Mechanics of Cas, University of Chinese Academy of Sciences, Xi’an 710119, China
Xi’an Institute of Lead Ir Technology Co., Ltd., Xi’an 710119, China
References (52)
Show source-provided references
1. Applications of computer vision techniques in the agriculture and food industry: A review10.1007/s00217-012-1844-2 ↗
2. Continuous body and hand gesture recognition for natural human-computer interaction10.1145/2133366.2133371 ↗
3. Efficient human pose estimation from single depth images10.1109/TPAMI.2012.241 ↗
4. Fastovets, M., Guillemaut, J.-Y., and Hilton, A. (2014, January 13–14). Athlete pose estimation by non-sequential key-frame propagation. Proceedings of the 11th European Conference on Visual Media Production, London, UK.10.1145/2668904.2668938 ↗
5. Chun, S., Ghalehjegh, N.H., Choi, J., Schwarz, C., Gaspar, J., McGehee, D., and Baek, S. (November, January 27). Nads-net: A nimble architecture for driver and seat belt detection via convolutional neural networks. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea.
6. Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv.
7. Predictively encoded graph convolutional network for noise-robust skeleton-based action recognition10.1007/s10489-021-02487-z ↗
8. Simonovsky, M., and Komodakis, N. (2017, January 21–26). Dynamic edge-conditioned filters in convolutional neural networks on graphs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.11 ↗
9. Maxwell, J.A., and Mittapalli, K. (2010). SAGE Handbook of Mixed Methods in Social &, Behavioral Research; Sage.
10. Multi–feature fusion tracking algorithm based on peak–context learning10.1016/j.imavis.2022.104468 ↗
11. A behavioral biometric system based on human-computer interaction10.1117/12.542625 ↗
12. Wu, S., Wang, J., Ping, Y., and Zhang, X. (2022, January 15–17). Research on individual recognition and matching of whale and dolphin based on efficientnet model. Proceedings of the 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Nanchang, China.10.1109/ICBAIE56435.2022.9985881 ↗
13. Zhang, X., Ping, Y., and Li, C. (2022, January 12–13). Artificial intelligence-based early warning method for abnormal operation and maintenance data of medical and health equipment. Proceedings of the IoT and Big Data Technologies for Health Care: Third EAI International Conference, IoTCare 2022, Virtual.10.1007/978-3-031-33545-7_22 ↗
14. Robust camera calibration for sport videos using court models10.1117/12.526813 ↗
15. A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities10.1016/j.eswa.2019.113114 ↗
16. Roussaki, I., Strimpakou, M., Kalatzis, N., Anagnostou, M., and Pils, C. (2006, January 13–17). Hybrid context modeling: A location-based scheme using ontologies. Proceedings of the Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW’06), Pisa, Italy.
17. Albert, J. (2010). Baseball data at season, play-by-play, and pitch-by-pitch levels. J. Stat. Educ., 18.10.1080/10691898.2010.11889592 ↗
18. Doroniewicz, I., Ledwoń, D.J., Affanasowicz, A., Kieszczyńska, K., Latos, D., Matyja, M., Mitas, A.W., and Myśliwiec, A. (2020). Writhing movement detection in newborns on the second and third day of life using pose-based feature machine learning classification. Sensors, 20.10.3390/s20215986 ↗
19. Toshev, A., and Szegedy, C. (2014, January 23–28). Deeppose: Human pose estimation via deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.214 ↗
20. Tompson, J.J., Jain, A., LeCun, Y., and Bregler, C. (2014). Joint training of a convolutional network and a graphical model for human pose estimation. Adv. Neural Inf. Process. Syst., 27.
21. Carreira, J., Agrawal, P., Fragkiadaki, K., and Malik, J. (2016, January 27–30). Human pose estimation with iterative error feedback. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.512 ↗
22. Farrukh, W., and Haar, D.v.d. (2021, January 17–19). Computer-assisted self-training for kyudo posture rectification using computer vision methods. Proceedings of the Fifth International Congress on Information and Communication Technology, London, UK.10.1007/978-981-15-5859-7_20 ↗
23. The impact of improving employee psychological empowerment and job performance based on deep learning and artificial intelligence10.4018/JOEUC.321639 ↗
24. Key-frame extraction techniques: A review10.2174/2213275911666180719111118 ↗
25. Tuning the sensitivity of genetically encoded fluorescent potassium indicators through structure-guided and genome mining strategies10.1021/acssensors.1c02201 ↗
26. Li, C., Chen, Z., and Jiao, Y. (2023). Vibration and bandgap behavior of sandwich pyramid lattice core plate with resonant rings. Materials, 16.10.3390/ma16072730 ↗
27. Realtime multi-person 2d pose estimation
28. Osokin, D. (2018). Real-time 2d multi-person pose estimation on cpu: Lightweight openpose. arXiv.10.5220/0007555407440748 ↗
29. Wei, S.-E., Ramakrishna, V., Kanade, T., and Sheikh, Y. (2016, January 27–30). Convolutional pose machines. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.511 ↗
30. Newell, A., Yang, K., and Deng, J. (November, January 27). Stacked hourglass networks for human pose estimation. Proceedings of the European Conference on Computer Vision, Seoul, Republic of Korea.
31. 3d skeleton-based human action classification: A survey10.1016/j.patcog.2015.11.019 ↗
32. Pavllo, D., Feichtenhofer, C., Grangier, D., and Auli, M. (2019, January 15–20). 3d human pose estimation in video with temporal convolutions and semi-supervised training. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00794 ↗
33. Gärtner, E., Pirinen, A., and Sminchisescu, C. (2020, January 7–12). Deep reinforcement learning for active human pose estimation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6714 ↗
34. Tsallis entropy-based information measures for shot boundary detection and keyframe selection10.1007/s11760-013-0452-3 ↗
35. Data clustering: 50 years beyond k-means10.1016/j.patrec.2009.09.011 ↗
36. Growing regression tree forests by classification for continuous object pose estimation10.1007/s11263-016-0942-1 ↗
37. Dual-expression system for blue fluorescent protein optimization10.1038/s41598-022-13214-0 ↗
38. Feature refinement and filter network for person re-identification10.1109/TCSVT.2020.3043026 ↗
39. Ning, X., Nan, F., Xu, S., Yu, L., and Zhang, L. (2020). Multi-view frontal face image generation: A survey. Concurr. Comput. Pract. Exp., e6147.10.1002/cpe.6147 ↗
40. Real-time 3d face alignment using an encoder-decoder network with an efficient deconvolution layer10.1109/LSP.2020.3032277 ↗
41. He, F., and Ye, Q. (2022). A bearing fault diagnosis method based on wavelet packet transform and convolutional neural network optimized by simulated annealing algorithm. Sensors, 22.10.20944/preprints202201.0068.v1 ↗
42. Chen, C.-C., Chang, C., Lin, C.-S., Chen, C.-H., and Chen, I.C. (2023). Video based basketball shooting prediction and pose suggestion system. Multimed. Tools Appl., 1–20.10.1007/s11042-023-14490-2 ↗
43. Fast 3d visualization of massive geological data based on clustering index fusion10.1109/ACCESS.2022.3157823 ↗
44. Fine segmentation on faces with masks based on a multistep iterative segmentation algorithm10.1109/ACCESS.2022.3192026 ↗
45. Reliability and validity of openpose for measuring hip-knee-ankle angle in patients with knee osteoarthritis10.1038/s41598-023-30352-1 ↗
46. The accuracy of markerless motion capture combined with computer vision techniques for measuring running kinematics10.1111/sms.14319 ↗
47. Human action recognition based on skeleton features10.2298/CSIS220131067G ↗
48. Fall detection based on openpose and mobilenetv2 network10.1049/ipr2.12667 ↗
49. Dewi, C., Chen, A.P.S., and Christanto, H.J. (2023). Deep learning for highly accurate hand recognition based on yolov7 model. Big Data Cogn. Comput., 7.10.3390/bdcc7010053 ↗
50. Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., and Sun, J. (2018, January 18–23). Cascaded pyramid network for multi-person pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00742 ↗
51. Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15–20). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00584 ↗
52. Martinez, J., Hossain, R., Romero, J., and Little, J.J. (2017, January 22–29). A simple yet effective baseline for 3d human pose estimation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.288 ↗