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access icon free Meta-action descriptor for action recognition in RGBD video

Action recognition is one of the hottest research topics in computer vision. Recent methods represent actions based on global or local video features. These approaches, however, lack semantic structure and may not provide a deep insight into the essence of an action. In this work, the authors argue that semantic clues, such as joint positions and part-level motion clustering, help verify actions. To this end, a meta-action descriptor for action recognition in RGBD video is proposed in this study. Specifically, two discrimination-based strategies – dynamic and discriminative part clustering – are introduced to improve accuracy. Experiments conducted on the MSR Action 3D dataset show that the proposed method significantly outperforms the methods without joint position semantic.

References

    1. 1)
      • 14. Yang, X., Zhang, C., Tian, Y.: ‘Recognizing actions using depth motion maps-based histograms of oriented gradients’. Proc. of the 20th ACM Int. Conf. on Multimedia, 2012, pp. 10571060.
    2. 2)
      • 22. Shahroudy, A., Ng, T.T., Yang, Q., et al: ‘Multimodal multipart learning for action recognition in depth videos’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (10), pp. 21232129.
    3. 3)
      • 12. Vieira, A.W., Nascimento, E.R., Oliveira, G.L., et al: ‘Stop: Space-time occupancy patterns for 3d action recognition from depth map sequences’. CIARP, 2012 (LNCS, 7441), pp. 252259.
    4. 4)
      • 13. Wang, J., Liu, Z., Chorowski, J., et al: ‘Robust 3d action recognition with random occupancy patterns’. Proc. of the 12th European Conf. on Computer Vision – Volume Part II, ECCV'12, Berlin, Heidelberg, 2012, pp. 872885.
    5. 5)
      • 29. Breiman, L.: ‘Out-of-bag estimation’. Tech. Rep., Citeseer, 1996.
    6. 6)
      • 28. Breiman, L.: ‘Bagging predictors’, Mach. Learn., 1996, 24, (2), pp. 123140.
    7. 7)
      • 20. Yang, X., Tian, Y.: ‘Eigenjoints-based action recognition using Naive-Bayes-nearest neighbor’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2012, 2012, pp. 1419.
    8. 8)
      • 25. Johansson, G.: ‘Visual perception of biological motion and a model for its analysis’, Percept. Psychophys., 1973, 14, (2), pp. 201211.
    9. 9)
      • 19. Xia, L., Chen, C.C., Aggarwal, J.: ‘View invariant human action recognition using histograms of 3d joints’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2012, 2012, pp. 2027.
    10. 10)
      • 4. Shotton, J., Girshick, R., Fitzgibbon, A., et al: ‘Efficient human pose estimation from single depth images’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 35, (12), pp. 28212840.
    11. 11)
      • 10. Jalal, A., Uddin, M.Z., Kim, J.T., et al: ‘Recognition of human home activities via depth silhouettes and R transformation for smart homes’, Indoor Built Environ., 2012, 21, (1), pp. 184190.
    12. 12)
      • 8. Yang, X., Tian, Y.: ‘Super normal vector for activity recognition using depth sequences’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, 2014, pp. 804811.
    13. 13)
      • 21. Vemulapalli, R., Arrate, F., Chellappa, R.: ‘Human action recognition by representing 3d skeletons as points in a lie group’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, 2014, pp. 588595.
    14. 14)
      • 7. Ofli, F., Chaudhry, R., Kurillo, G., et al: ‘Sequence of the most informative joints (smij): a new representation for human skeletal action recognition’, J. Vis. Commun. Image Represent., 2014, 25, (1), pp. 2438.
    15. 15)
      • 23. Luo, J., Wang, W., Qi, H.: ‘Group sparsity and geometry constrained dictionary learning for action recognition from depth maps’. IEEE Int. Conf. on Computer Vision (ICCV), 2013, 2013, pp. 18091816.
    16. 16)
      • 1. Poppe, R.: ‘A survey on vision-based human action recognition’, Image Vis. Comput., 2010, 28, (6), pp. 976990.
    17. 17)
      • 26. Wang, H., Klser, A., Schmid, C., et al: ‘Dense trajectories and motion boundary descriptors for action recognition’, Int. J. Comput. Vis., 2013, 103, (1), pp. 6079.
    18. 18)
      • 17. Oreifej, O., Liu, Z.: ‘Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013, 2013, pp. 716723.
    19. 19)
      • 2. Chen, L., Wei, H., Ferryman, J.: ‘A survey of human motion analysis using depth imagery’, Pattern Recognit. Lett., 2013, 34, (15), pp. 19952006.
    20. 20)
      • 30. Huang, M., Cai, G.R., Zhang, H.B., et al: ‘Semantic and discriminative parts learning for 3d human action recognition’, Under Review, 2016.
    21. 21)
      • 3. Shotton, J., Fitzgibbon, A., Cook, M., et al: ‘Real-time human pose recognition in parts from single depth images’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011, 2011, pp. 12971304.
    22. 22)
      • 6. Rahmani, H., Mahmood, A., Huynh, D.Q., et al: ‘Hopc: histogram of oriented principal components of 3d pointclouds for action recognition’. Computer Vision-ECCV 2014, 2014, pp. 742757.
    23. 23)
      • 18. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’. Advances in Neural Information Processing Systems, 2012, pp. 10971105.
    24. 24)
      • 5. Wang, J., Liu, Z., Wu, Y., et al: ‘Mining actionlet ensemble for action recognition with depth cameras’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2012, 2012, pp. 12901297.
    25. 25)
      • 16. Yang, R., Yang, R.: ‘Dmm-pyramid based deep architectures for action recognition with depth cameras’. 12th Asian Conf. on Computer Vision – ACCV 2014, 2014, pp. 3749.
    26. 26)
      • 27. Breiman, L.: ‘Random forests’, Mach. Learn., 2001, 45, (1), pp. 532.
    27. 27)
      • 15. Wang, P., Li, W., Gao, Z., et al: ‘Convnets-based action recognition from depth maps through virtual cameras and pseudocoloring’. Proc. of the 23rd ACM Int. Conf. on Multimedia, MM'15, New York, USA, 2015, pp. 11191122.
    28. 28)
      • 9. Li, W., Zhang, Z., Liu, Z.: ‘Action recognition based on a bag of 3d points’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2010, 2010, pp. 914.
    29. 29)
      • 24. Wang, J., Nie, X., Xia, Y., et al: ‘Cross-view action modeling, learning and recognition’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, 2014, pp. 26492656.
    30. 30)
      • 11. Lu, C., Jia, J., Tang, C.K.: ‘Range-sample depth feature for action recognition’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, 2014, pp. 772779.
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