• Laser & Optoelectronics Progress
  • Vol. 58, Issue 24, 2410010 (2021)
Guoyin Ren1、2, Xiaoqi Lü1、2、3、*, and Yuhao Li2
Author Affiliations
  • 1School of Mechanical Engineering, Inner Mongolia University of Science & Technology, Baotou, Inner Mongolia 0 14010, China;
  • 2School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, Inner Mongolia 0 14010, China;
  • 3Inner Mongolia University of Technology, Huhhot, Inner Mongolia 0 10051, China
  • show less
    DOI: 10.3788/LOP202158.2410010 Cite this Article Set citation alerts
    Guoyin Ren, Xiaoqi Lü, Yuhao Li. Multi-Feature Fusion Real-Time Action Recognition Based on 2D to 3D Skeleton[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2410010 Copy Citation Text show less
    References

    [1] Zeng S, Geng G H, Zou L B et al. Real spatial terrain reconstruction of first person point-of-view sketches[J]. Optics and Precision Engineering, 28, 1861-1871(2020).

    [2] Zhou M Q, Fan Y C, Geng G H. A spatial symmetry descriptor for 3D model[J]. Acta Electronica Sinica, 38, 853-859(2010).

    [3] Li M W, Shi H Q. Multispectral palmprint fusion recognition based on local joint edge and orientation patterns[J]. Journal of Changchun Normal University, 39, 69-80(2020).

    [4] Jiang C, Geng Z X, Lou B et al. Automatic image stitching based on scale invariant feature[J]. Remote Sensing Information, 28, 20-25(2013).

    [5] Li Y M, Su L. A grid map merging approach based on local feature[J]. Computer Applications and Software, 37, 110-115(2020).

    [6] Huang J J, Ding Y J. Improved video optical flow field estimation based on wavelet transform and HSI[J]. Modern Electronics Technique, 33, 117-120(2010).

    [7] Xiong J L, Wang C. Simultaneous localization and mapping based on RGB-D images with filter processing and pose optimization[J]. Journal of University of Science and Technology of China, 47, 665-673(2017).

    [8] Chen C H, Ramanan D. 3D human pose estimation=2D pose estimation + matching[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 5759-5767(2017).

    [9] Martinez J, Hossain R, Romero J et al. A simple yet effective baseline for 3D human pose estimation[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 2659-2668(2017).

    [10] Moreno-Noguer F. 3D human pose estimation from a single image via distance matrix regression[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 1561-1570(2017).

    [11] Mehta D, Sridhar S, Sotnychenko O et al. VNect: real-time 3D human pose estimation with a single RGB camera[J]. ACM Transactions on Graphics, 36, 44(2017).

    [12] Sun X, Shang J X, Liang S et al. Compositional human pose regression[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 2621-2630(2017).

    [13] Yang J, Zhang S J, Zhang C H et al. Research on human action recognition and contrast based on OpenPose[J]. Transducer and Microsystem Technologies, 40, 5-8(2021).

    [14] Cippitelli E, Gasparrini S, Gambi E et al. A human activity recognition system using skeleton data from RGBD sensors[J]. Computational Intelligence and Neuroscience, 2016, 4351435(2016).

    [15] Aubry S, Laraba S, Tilmanne J et al. Action recognition based on 2D skeletons extracted from RGB videos[J]. MATEC Web of Conferences, 277, 02034(2019).

    [16] Pham H H, Salmane H, Khoudour L et al. A unified deep framework for joint 3D pose estimation and action recognition from a single RGB camera[J]. Sensors, 20, 1825(2020).

    [17] Yang F, Wu Y, Sakti S et al. Make skeleton-based action recognition model smaller, faster and better[C]. //Proceedings of the ACM Multimedia Asia, December 16-18, 2019, Beijing, China, 1-6(2019).

    [18] Ionescu C, Papava D, Olaru V et al. Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1325-1339(2014).

    [19] Zhang H W, Yu Z Z, Lei W W. Study on the basic theory of quaternion and Eulerian equation of the rotating vector[J]. Journal of Geodesy and Geodynamics, 40, 502-506(2020).

    [20] Shahroudy A, Liu J, Ng T T et al. NTU RGB+D: a large scale dataset for 3D human activity analysis[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA, 1010-1019(2016).

    [21] Sigal L, Balan A O, Black M J. HumanEva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion[J]. International Journal of Computer Vision, 87, 4-27(2009).

    [22] Luvizon D C, Picard D, Tabia H. 2D/3D pose estimation and action recognition using multitask deep learning[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 5137-5146(2018).

    [23] Guo F Z, Kong J, Jiang M. Action recognition based on adaptive fusion of RGB and skeleton features[J]. Laser & Optoelectronics Progress, 57, 201506(2020).

    [24] Liu F, Yu F Q. Human action recognition based on global and local features[J]. Laser & Optoelectronics Progress, 57, 021004(2020).

    [25] Hou C P, Jiang T L, Lang Y et al. Human activity and identity multi-task recognition based on convolutional neural network using Doppler radar[J]. Laser & Optoelectronics Progress, 57, 021009(2020).

    [26] Huang Y W, Wang F, Li J H et al. Algorithm for video temporal action proposal combining watershed and regression networks[J]. Chinese Journal of Lasers, 46, 1109001(2019).

    [27] Li Y, Yang D D, Han Y J et al. Siamese neural network object tracking with distractor-aware model[J]. Acta Optica Sinica, 40, 0415002(2020).

    [28] Song Y F, Zhang Z, Wang L. Richly activated graph convolutional network for action recognition with incomplete skeletons[C]. //2019 IEEE International Conference on Image Processing (ICIP), September 22-25, 2019, Taipei, China., 1-5(2019).

    [29] Li M S, Chen S H, Chen X et al. Actional-structural graph convolutional networks for skeleton-based action recognition[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 3590-3598(2019).

    [30] Shi L, Zhang Y F, Cheng J et al. Two-stream adaptive graph convolutional networks for skeleton-based action recognition[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, Long Beach, CA, USA., 12018-12027(2019).

    [31] Zhu G M, Zhang L, Li H S et al. Topology-learnable graph convolution for skeleton-based action recognition[J]. Pattern Recognition Letters, 135, 286-292(2020).

    [32] Yang W J, Zhang J L, Cai J J et al. Shallow graph convolutional network for skeleton-based action recognition[J]. Sensors, 21, 452(2021).

    Guoyin Ren, Xiaoqi Lü, Yuhao Li. Multi-Feature Fusion Real-Time Action Recognition Based on 2D to 3D Skeleton[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2410010
    Download Citation