Human action recognition based on mocap information using convolution neural networks

Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Volumn , Issue , 2014, Pages 159-164

  Ijjina, Earnest Paul ^a   Mohan, C Krishna ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY HYDERABAD   (India)

Author keywords

convolutional neural networks (CNN); motion capture (MOCAP)

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONVOLUTION; GESTURE RECOGNITION; IMAGE RECOGNITION; INFORMATION USE; LEARNING SYSTEMS; MOTION ESTIMATION; NEURAL NETWORKS; SEMANTICS;

ACTION RECOGNITION; CONVOLUTION NEURAL NETWORK; CONVOLUTIONAL NEURAL NETWORK; DISTANCE METRICS; HUMAN-ACTION RECOGNITION; MOTION CAPTURE; SEMANTIC ANALYSIS; TEMPORAL VARIATION;

BEHAVIORAL RESEARCH;

EID: 84946686872     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2014.30     Document Type: Conference Paper

References (17)

1. J. Han, L. Shao, D. Xu, and J. Shotton, Enhanced computer vision with microsoft kinect sensor: A review. IEEE Transactions on Cybernetics, vol. 43, no. 5, pp. 1318-1334, June 2013
2. K. Li, Q. Dai, and W. Xu, Markerless shape and motion capture from multiview video sequences. IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 3, pp. 320-334, Mar. 2011
3. H. Jhuang, J. Gall, S. Zuffi, C. Schmid, and M. J. Black, Towards understanding action recognition. in IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, Dec. 2013, pp. 3192-3199
4. F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, Sequence of the most informative joints (smij): A new representation for human skeletal action recognition. in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, June 2012, pp. 8-13
5. Z. Jiang, Z. Lin, and L. S. Davis, Label consistent k-svd: Learning a discriminative dictionary for recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2651-2664, Nov. 2013
6. W. Lin, H. Chu, J. Wu, B. Sheng, and Z. Chen, A heat-mapbased algorithm for recognizing group activities in videos. IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 11, pp. 1980-1992, Nov. 2013
7. A. Prest, V. Ferrari, and C. Schmid, Explicit modeling of human-object interactions in realistic videos, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 4, pp. 835-848, April 2013
8. S. Ji, W. Xu, M. Yang, and K. Yu, 3d convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 221-231, Jan. 2013
9. Y. Zhu, W. Chen, and G. Guo, Fusing spatiotemporal features and joints for 3d action recognition, in IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, June 2013, pp. 486-491
10. K. Cho and X. Chen, Classifying and visualizing motion capture sequences using deep neural networks, Computer Research Repository (CoRR), vol. abs/1306. 3874, 2013
11. K. Yun, J. Honorio, D. Chattopadhyay, T. L. Berg, and D. Samaras, Two-person interaction detection using bodypose features and multiple instance learning. in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, June 2012, pp. 28-35
12. L. Xia, C.-C. Chen, and J. K. Aggarwal, View invariant human action recognition using histograms of 3d joints, in Workshop on Human Activity Understanding from 3D Data in conjunction with CVPR (HAU3D), Rhode Island, USA, 2012, pp. 20-27
13. Y. Bengio, Learning deep architectures for ai, Foundation and Trends in Machine Learning, vol. 2, no. 1, pp. 1-127, Jan. 2009
14. R. B. Palm, Prediction as a candidate for learning deep hierarchical models of data, Masters thesis, Technical University of Denmark, Asmussens Alle, Denmark, 2012
15. F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, Berkeley mhad: A comprehensive multimodal human action database, in 2013 IEEE Workshop on Applications of Computer Vision (WACV), Jan 2013, pp. 53-60
16. M. S. Cheema, A. Eweiwi, and C. Bauckhage, Human activity recognition by separating style and content, Pattern Recognition Letters, In Press
17. R. Chaudhry, F. Ofli, G. Kurillo, R. Bajcsy, and R. Vidal, Bio-inspired dynamic 3d discriminative skeletal features for human action recognition, in 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 2013, pp. 471-478