3D ACTION RECOGNITION USING DATA VISUALIZATION AND CONVOLUTIONAL NEURAL NETWORKS

被引:0
作者
Liu, Mengyuan [1 ]
Chen, Chen [2 ]
Liu, Hong [1 ]
机构
[1] Peking Univ, Shenzhen Grad Sch, Key Lab Machine Percept, Shenzhen, Peoples R China
[2] Univ Cent Florida, Ctr Comp Vis Res, Orlando, FL 32816 USA
来源
2017 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME) | 2017年
基金
中国国家自然科学基金;
关键词
3D action recognition; data visualization; skeleton data; convolutional neural networks; DEPTH; SENSOR; FUSION;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
It remains a challenge to efficiently represent spatial-temporal data for 3D action recognition. To solve this problem, this paper presents a new skeleton-based action representation using data visualization and convolutional neural networks, which contains four main stages. First, skeletons from an action sequence are mapped as a set of five dimensional points, containing three dimensions of location, one dimension of time label and one dimension of joint label. Second, these points are encoded as a series of color images, by visualizing points as RGB pixels. Third, convolutional neural networks are adopted to extract deep features from color images. Finally, action class score is calculated by fusing selected deep features. Extensive experiments on three benchmark datasets show that our method achieves state-of-the-art results.
引用
收藏
页码:925 / 930
页数:6
相关论文
共 35 条
  • [1] [Anonymous], 2014, ADV NEURAL INFORM PR
  • [2] [Anonymous], ACM MM
  • [3] [Anonymous], CVPRW
  • [4] [Anonymous], 2015, ACM INT C MULT, DOI DOI 10.1145/2733373.2807412
  • [5] [Anonymous], 2014, 2014 IEEE C COMPUTER, DOI DOI 10.1109/CVPR.2014.82
  • [6] [Anonymous], CVPR
  • [7] [Anonymous], 2012, P SIGCHI C HUM FACT
  • [8] Dynamic Image Networks for Action Recognition
    Bilen, Hakan
    Fernando, Basura
    Gavves, Efstratios
    Vedaldi, Andrea
    Gould, Stephen
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 3034 - 3042
  • [9] Chen C.R. J., 2015, Multimedia Tools and Applications, P1
  • [10] A Real-Time Human Action Recognition System Using Depth and Inertial Sensor Fusion
    Chen, Chen
    Jafari, Roozbeh
    Kehtarnavaz, Nasser
    [J]. IEEE SENSORS JOURNAL, 2016, 16 (03) : 773 - 781