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Volume 41 Issue 6
Jun.  2015
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(6): 1122-1127.
WU Li-cheng, LU Zhen, YU Shou-qian, et al. Novel Neural Networks Control Approach of Flexible Manipulators[J]. Journal of Beijing University of Aeronautics and Astronautics, 2002, 28(3): 316-318. (in Chinese)
Citation: BAI Chen, SUN Junhua. Human action recognition based on locality-constrained linear coding[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(6): 1122-1127. doi: 10.13700/j.bh.1001-5965.2014.0414(in Chinese)
shu

Human action recognition based on locality-constrained linear coding

doi: 10.13700/j.bh.1001-5965.2014.0414

  • School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 10 Jul 2014
  • Publish Date: 20 Jun 2015
    Abstract
  • Large intra-class variations of action features lead to low classification accuracy of action recognition, on the other hand, current algorithms exist drawbacks in computational complexity and extension of recognizable action classes. A method based on locality-constrained linear coding (LLC) for action recognition from depth images was proposed. In order to reduce the intra-class variations and increase classification accuracy, joints' positions, velocities and acceleration features were concatenated to form local action features, then LLC was used to calculate sparse representations of local action features. Analytical solution of LLC ensures computational speed of our method is up to 760 frames per second. Dictionary is composed by sub-dictionaries learned by K-means from features of each class separately, so global optimization is avoided during extending recognizable action classes. Moreover, to avoid classifier to be over-fitting, a dimensionality reduction method based on labels of dictionary items was proposed. The proposed method was evaluated on MSR-Action3D dataset captured by depth cameras. The experimental results show that the proposed approach achieves classification accuracy of 85.7%.

     

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    • References(22)
  • [1]
    郑韡, 沈旭昆.基于连续数据流的动态手势识别算法[J].北京航空航天大学学报, 2012, 38(2):273-279. Zheng W, Shen X K.Algorithm based on continuous data stream for dynamic gesture recognition[J].Journal of Beijing University of Aeronautics and Astronautics, 2012, 38(2):273-279(in Chinese).
    [2]
    史骏, 陈才扣.基于马氏距离的半监督鉴别分析及人脸识别[J].北京航空航天大学学报, 2011, 37(12):1589-1593. Shi J, Chen C K.Mahalanobis distance-based semi-supervised discriminant analysis for face recognition[J].Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(12):1589-1593(in Chinese).
    [3]
    Weinland D, Ronfard R, Boyer E.A survey of vision-based methods for action representation, segmentation and recognition[J].Computer Vision and Image Understanding, 2011, 115(2):224-241.
    [4]
    Shotton J, Girshick R, Fitzgibbon A, et al.Efficient human pose estimation from single depth images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(12):2821-2840.
    [5]
    Jhuang H, Gall J, Zuffi S, et al.Towards understanding action recognition[C]//Proceedings of IEEE International Conference on Computer Vision (ICCV).Piscataway, NJ:IEEE Press, 2013:3192-3199.
    [6]
    Xia L, Chen C C, Aggarwal J K.View invariant human action recognition using histograms of 3d joints[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Piscataway, NJ:IEEE Press, 2012:20-27.
    [7]
    Yang X, Tian Y L.Eigenjoints-based action recognition using naïve bayes nearest neighbor[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Piscataway, NJ:IEEE Press, 2012:14-19.
    [8]
    Wang J, Liu Z, Wu Y, et al.Mining actionlet ensemble for action recognition with depth cameras[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway, NJ:IEEE Press, 2012:1290-1297.
    [9]
    Zanfir M, Leordeanu M, Sminchisescu C.The moving pose:An efficient 3D kinematics descriptor for low-latency action recognition and detection[C]//Proceedings of IEEE International Conference on Computer Vision(ICCV).Piscataway, NJ:IEEE Press, 2013:2752-2759.
    [10]
    Luo J, Wang W, Qi H.Group sparsity and geometry constrained dictionary learning for action recognition from depth maps[C]//Proceedings of IEEE International Conference on Computer Vision(ICCV).Piscataway, NJ:IEEE Press, 2013:1089-1816.
    [11]
    Wang J, Yang J, Yu K, et al.Locality-constrained linear coding for image classification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway, NJ:IEEE Press, 2010:3360-3367.
    [12]
    Yu K, Zhang T, Gong Y.Nonlinear learning using local coordinate coding[C]//Advances in Neural Information Processing Systems.La Jolla, CA:Neural Information Processing Systems Foundation, 2009:1-9.
    [13]
    Chang C C, Lin C J.LIBSVM:A library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology(TIST), 2011, 2(3):27.
    [14]
    Martens J, Sutskever I.Learning recurrent neural networks with Hessian-free optimization[C]//Proceedings of the 28th International Conference on Machine Learning(ICML).New York:International Machine Learning Society(IMLS), 2011:1033-1040.
    [15]
    Müller M, Röder T.Motion templates for automatic classification and retrieval of motion capture data[C]//Proceedings of the ACM SIGGRAPH.New York:ACM, 2006:137-146.
    [16]
    Lv F, Nevatia R.Recognition and segmentation of 3-d human action using hmm and multi-class adaboost[C]//Proceedings of European Conference on Computer Vision(ECCV).Berlin, Heidelberg:Springer, 2006:359-372.
    [17]
    Morency L, Quattoni A, Darrell T.Latent-dynamic discriminative models for continuous gesture recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2007:1-8.
    [18]
    Li W, Zhang Z, Liu Z.Action recognition based on a bag of 3d points[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Piscataway, NJ:IEEE Press, 2010:9-14.
    [19]
    Vieira A W, Nascimento E R, Oliveira G L, et al.Stop:space-time occupancy patterns for 3d action recognition from depth map sequences[C]//Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications.Berlin, Heidelberg:Springer, 2012:252-259.
    [20]
    Wang J, Liu Z, Chorowski J, et al.Robust 3d action recognition with random occupancy patterns[C]//Proceedings of European Conference on Computer Vision(ECCV).Berlin, Heidelberg:Springer, 2012:872-885.
    [21]
    Mairal J, Bach F, Ponce J, et al.Online dictionary learning for sparse coding[C]//Proceedings of the 26th Annual International Conference on Machine Learning.New York:ACM, 2009:689-696.
    [22]
    Lee H, Battle A, Raina R, et al.Efficient sparse coding algorithms[C]//Advances in Neural Information Processing Systems.La Jolla, CA:Neural Information Processing Systems Foundation, 2006:801-808.
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