Hierarchical Gaussian descriptor based on local pooling for action recognition

Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. TPAMI 28(12), 2037–2041 (2006)

Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Geometric means in a novel vector space structure on symmetric positive definite matrices. SIAM J. Matrix Anal. Appl. 29(1), 328–347 (2007)

Bilinski, P., Bremond, F.: Video covariance matrix logarithm for human action recognition in videos. In: IJCAI, pp. 2140–2147 (2015)

Boureau, Y.L., Roux, N.L., Bach, F., Ponce, J., LeCun, Y.: Ask the locals: multi-way local pooling for image recognition. In: ICCV, pp. 2651–2658 (2011)

Cao, Z., Simon, T., Wei, S., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR, pp. 1302–1310 (2017)

Cavazza, J., Zunino, A., Biagio, M.S., Murino, V.: Kernelized covariance for action recognition. In: ICPR, pp. 408–413 (2016)

Chen, C., Jafari, R., Kehtarnavaz, N.: Action recognition from depth sequences using depth motion maps-based local binary patterns. In: WACV, pp. 1092–1099 (2015)

Chen, C., Liu, K., Kehtarnavaz, N.: Real-time human action recognition based on depth motion maps. J. Real Time Image Process. 12(1), 155–163 (2016)

Cirujeda, P., Binefa, X.: 4DCov: a nested covariance descriptor of spatio-temporal features for gesture recognition in depth sequences. In: 3DV, vol. 1, pp. 657–664 (2014)

Coates, A., Ng, A.Y.: The importance of encoding versus training with sparse coding and vector quantization. In: ICML, pp. 921–928 (2011)

Davis, L.S.: Covariance discriminative learning: a natural and efficient approach to image set classification. In: CVPR, pp. 2496–2503 (2012)

Devanne, M., Wannous, H., Berretti, S., Pala, P., Daoudi, M., Bimbo, A.D.: 3-d human action recognition by shape analysis of motion trajectories on Riemannian manifold. IEEE Trans. Cybern. 45(7), 1340–1352 (2015)

Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72 (2005)

Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: CVPR, pp. 1110–1118 (2015)

Evangelidis, G., Singh, G., Horaud, R.: Skeletal quads: human action recognition using joint quadruples. In: ICPR, pp. 4513–4518 (2014)

Fan, K.C., Hung, T.Y.: A novel local pattern descriptor—local vector pattern in high-order derivative space for face recognition. IEEE Trans. Image Process. 23, 2877–2891 (2014)

Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

Gao, Z., Zhang, H., Xu, G., Xue, Y.: Multi-perspective and multi-modality joint representation and recognition model for 3D action recognition. Neurocomputing 151, 554–564 (2015)

Gong, L., Wang, T., Liu, F.: Shape of Gaussians as feature descriptors. In: CVPR, pp. 2366–2371 (2009)

Gowayyed, M.A., Torki, M., Hussein, M.E., El-Saban, M.: Histogram of oriented displacements (HOD): describing trajectories of human joints for action recognition. In: IJCAI, pp. 1351–1357 (2013)

Guo, K., Ishwar, P., Konrad, J.: Action recognition from video using feature covariance matrices. IEEE Trans. Image Process. 22(6), 2479–2494 (2013)

Harandi, M.T., Salzmann, M., Hartley, R.: From manifold to manifold: geometry-aware dimensionality reduction for SPD matrices. In: ECCV, pp. 17–32 (2014)

Harandi, M.T., Sanderson, C., Sanin, A., Lovell, B.C.: Spatio-temporal covariance descriptors for action and gesture recognition. In: WACV, pp. 103–110 (2013)

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

Holte, M.B., Moeslund, T.B., Fihl, P.: View-invariant gesture recognition using 3D optical flow and harmonic motion context. CVIU 114(12), 1353–1361 (2010)

Huang, Z., Gool, L.V.: A Riemannian network for SPD matrix learning. In: AAAI, pp. 2036–2042 (2017)

Huang, Z., Wan, C., Probst, T., Gool, L.V.: Deep learning on Lie groups for skeleton-based action recognition. In: CVPR (2017)

Huang, Z., Wu, J., Gool, L.V.: Building deep networks on Grassmann manifolds. In: AAAI (2018)

Hussein, M.E., Torki, M., Gowayyed, M.A., El-Saban, M.: Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations. In: IJCAI, pp. 2466–2472 (2013)

Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: ICCV, pp. 2146–2153 (2009)

Jaynes, E.T.: Information theory and statistical mechanics. Phys. Rev. 106(4), 620–630 (1957)

Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: CVPR, pp. 3304–3311 (2010)

Klaser, A., Marszalek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: BMVC, pp. 1–10 (2008)

Kurakin, A., Zhang, Z., Liu, Z.: A real time system for dynamic hand gesture recognition with a depth sensor. In: EUSIPCO, pp. 1975–1979 (2012)

Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: ICML, pp. 609–616 (2009)

Li, P., Wang, Q.: Local log-Euclidean covariance matrix (L2ECM) for image representation and its applications. In: ECCV, pp. 469–482 (2012)

Li, P., Wang, Q., Zeng, H., Zhang, L.: Local log-Euclidean multivariate Gaussian descriptor and its application to image classification. TPAMI 39(4), 803–817 (2017)

Li, P., Zeng, H., Wang, Q., Shiu, S.C.K., Zhang, L.: High-order local pooling and encoding Gaussians over a dictionary of Gaussians. IEEE Trans. Image Process. 26(7), 3372–3384 (2017)

Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: CVPRW, pp. 9–14 (2010)

Lin, Y.C., Hu, M.C., Cheng, W.H., Hsieh, Y.H., Chen, H.M.: Human action recognition and retrieval using sole depth information. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 1053–1056 (2012)

Liu, A., Nie, W., Su, Y., Ma, L., Hao, T., Yang, Z.: Coupled hidden conditional random fields for RGB-D human action recognition. Signal Process. 112(C), 74–82 (2015)

Liu, C., Hu, Y., Li, Y., Song, S., Liu, J.: PKU-MMD: a large scale benchmark for continuous multi-modal human action understanding. CoRR (2017). arXiv:1703.07475

Liu, J., Wang, G., Hu, P., Duan, L.Y., Kot, A.C.: Global context-aware attention LSTM networks for 3D action recognition. In: CVPR, pp. 3671–3680 (2017)

Liu, L., Shao, L.: Learning discriminative representations from RGB-D video data. In: IJCAI, pp. 1493–1500 (2013)

Liu, M., Liu, H., Chen, C.: 3D action recognition using multi-scale energy-based global ternary image. IEEE Trans. Circuits Syst. Video Technol. 28(8), 1824–1838 (2018)

Lovrić, M., Min-Oo, M., Ruh, E.A.: Multivariate normal distributions parametrized as a Riemannian symmetric space. J. Multivar. Anal. 74(1), 36–48 (2000)

Luo, C., Ma, C., Wang, C., Wang, Y.: Learning discriminative activated simplices for action recognition. In: AAAI, pp. 4211–4217 (2017)

Luo, J., Wang, W., Qi, H.: Group sparsity and geometry constrained dictionary learning for action recognition from depth maps. In: ICCV, pp. 1809–1816 (2013)

Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: ICML, pp. 689–696 (2009)

Mairal, J., Bach, F., Ponce, J., Sapiro, G., Jenatton, R., Obozinski, G.: SPAMS: SPArse modeling software, v2.4 (2014). http://spams-devel.gforge.inria.fr/downloads.html

Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y.: Hierarchical Gaussian descriptor for person re-identification. In: CVPR, pp. 1363–1372 (2016)

Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. TPAMI 27(10), 1615–1630 (2005)

Müller, M., Röder, T., Clausen, M., Eberhardt, B., Krüger, B., Weber, A.: Documentation Mocap Database HDM05. Technical Report CG-2007-2, Universität Bonn (2007)

Nguyen, X., Mouaddib, A.I., Nguyen, T., Jeanpierre, L.: Action recognition in depth videos using hierarchical Gaussian descriptor. Multimedia Tools Appl. 77(16), 21617–21652 (2018)

Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 1, pp. 582–585 (1994)

Oneata, D., Verbeek, J., Schmid, C.: Action and event recognition with Fisher vectors on a compact feature set. In: ICCV, pp. 1817–1824 (2013)

Oreifej, O., Liu, Z.: HON4D: Histogram of oriented 4D normals for activity recognition from depth sequences. In: CVPR, pp. 716–723 (2013)

Pang, Y., Yuan, Y., Li, X.: Gabor-based region covariance matrices for face recognition. IEEE Trans. Circuits Syst. Video Technol. 18(7), 989–993 (2008)

Rahmani, H., Mian, A.: 3D action recognition from novel viewpoints. In: CVPR, pp. 1506–1515 (2016)

Sanchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the Fisher vector: theory and practice. IJCV 105(3), 222–245 (2013)

Seidenari, L., Varano, V., Berretti, S., Del Bimbo, A., Pala, P.: Recognizing actions from depth cameras as weakly aligned multi-part bag-of-poses. In: CVPRW, pp. 479–485 (2013)

Serra, G., Grana, C., Manfredi, M., Cucchiara, R.: GOLD: Gaussians of local descriptors for image representation. CVIU 134, 22–32 (2015)

Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: NTU RGB+D: a large scale dataset for 3D human activity analysis. In: CVPR, pp. 1010–1019 (2016)

Shi, L., Zhang, Y., Cheng, J., Lu, H.: Adaptive spectral graph convolutional networks for skeleton-based action recognition. CoRR (2018). arXiv:1805.07694

Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: CVPR, pp. 1297–1304 (2011)

Tang, S., Wang, X., Lv, X., Han, T.X., Keller, J., He, Z., Skubic, M., Lao, S.: Histogram of oriented normal vectors for object recognition with a depth sensor. In: ACCV, pp. 525–538 (2013)

Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. ECCV, Part II, pp. 589–600 (2006)

Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on Riemannian manifolds. TPAMI 30(10), 1713–1727 (2008)

Vedaldi, A., Fulkerson, B.: Vlfeat: an open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1469–1472 (2010)

Vemulapalli, R., Arrate, F., Chellappa, R.: Human action recognition by representing 3D skeletons as points in a Lie group. In: CVPR, pp. 588–595 (2014)

Wang, C., Flynn, J., Wang, Y., Yuille, A.L.: Recognizing actions in 3D using action-snippets and activated simplices. In: AAAI, pp. 3604–3610 (2016)

Wang, C., Wang, Y., Yuille, A.L.: An approach to pose-based action recognition. In: CVPR, pp. 915–922 (2013)

Wang, C., Wang, Y., Yuille, A.L.: Mining 3D key-pose-motifs for action recognition. In: CVPR, pp. 2639–2647 (2016)

Wang, J., Liu, Z., Chorowski, J., Chen, Z., Wu, Y.: Robust 3D action recognition with random occupancy patterns. In: ECCV, pp. 872–885 (2012)

Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: CVPR, pp. 1290–1297 (2012)

Wang, L., Zhang, J., Zhou, L., Tang, C., Li, W.: Beyond covariance: feature representation with nonlinear kernel matrices. In: ICCV, pp. 4570–4578 (2015)

Wang, P., Li, W., Gao, Z., Zhang, J., Tang, C., Ogunbona, P.O.: Action recognition from depth maps using deep convolutional neural networks. IEEE Trans. Hum. Mach. Syst. 46(4), 498–509 (2016)

Wang, Q., Li, P., Zhang, L., Zuo, W.: Towards effective codebookless model for image classification. Pattern Recognit. 59(C), 63–71 (2016)

Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse representation for computer vision and pattern recognition. Proc. IEEE 98(6), 1031–1044 (2010)

Xia, L., Aggarwal, J.K.: Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: CVPR, pp. 2834–2841 (2013)

Xia, L., Chen, C.C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3D joints. In: CVPRW, pp. 20–27 (2012)

Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: AAAI (2018)

Yang, X., Tian, Y.: Super normal vector for activity recognition using depth sequences. In: CVPR, pp. 804–811 (2014)

Yang, X., Tian, Y.L.: EigenJoints-based action recognition using Naive–Bayes-nearest-neighbor. In: CVPRW, pp. 14–19 (2012)

Yang, X., Zhang, C., Tian, Y.: Recognizing actions using depth motion maps-based histograms of oriented gradients. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 1057–1060 (2012)

Yi, Y., Wang, H.: Motion keypoint trajectory and covariance descriptor for human action recognition. Vis. Comput. 34(3), 391–403 (2018)

Yu, M., Liu, L., Shao, L.: Structure-preserving binary representations for RGB-D action recognition. TPAMI 38(8), 1651–1664 (2016)

Yuan, C., Hu, W., Li, X., Maybank, S., Luo, G.: Human action recognition under log-Euclidean Riemannian metric. In: ACCV, pp. 343–353 (2010)

Zanfir, M., Leordeanu, M., Sminchisescu, C.: The moving pose: an efficient 3D kinematics descriptor for low-latency action recognition and detection. In: ICCV, pp. 2752–2759 (2013)

Zhang, C., Tian, Y.: Histogram of 3D facets. CVIU 139(C), 29–39 (2015)

Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. TPAMI 29(6), 915–928 (2007)

Zhou, X., Yu, K., Zhang, T., Huang, T.S.: Image classification using super-vector coding of local image descriptors. In: ECCV, pp. 141–154 (2010)