Classifying actions based on histogram of oriented velocity vectors

Journal of Intelligent Information Systems

Volumn 44, Issue 1, 2015, Pages 49-65

  Boubou, Somar ^a   Suzuki, Einoshin ^a  

^a KYUSHU UNIVERSITY   (Japan)

Author keywords

Conditional random fields; Extreme learning machines; Feature extraction; Feature histogram; HON4D; Human action recognition; Kinect; Skeletal data

Indexed keywords

EXTRACTION; FEATURE EXTRACTION; GRAPHIC METHODS; KNOWLEDGE ACQUISITION; MUSCULOSKELETAL SYSTEM; NEAREST NEIGHBOR SEARCH; SUPPORT VECTOR MACHINES; VECTORS;

CONDITIONAL RANDOM FIELD; EXTREME LEARNING MACHINE; FEATURE HISTOGRAM; HON4D; HUMAN-ACTION RECOGNITION; KINECT; SKELETAL DATA;

LEARNING SYSTEMS;

EID: 84921699346     PISSN: 09259902     EISSN: 15737675     Source Type: Journal    
DOI: 10.1007/s10844-014-0329-0     Document Type: Article

References (43)

1. Aggarwal, J., & Ryoo, M. (2011). Human activity analysis: a review. ACM Computing Surveys (CSUR), 43(3), 1–43.
2. Ahmad, M., & Lee, S. (2006). HMM-based human action recognition using multiview image sequences. In Proceedings of International Conference Pattern Recognition (ICPR), (vol. 1, pp. 263–266).
3. Chen, X. (2013). Online RGB-D gesture recognition with extreme learning machines. In Proceedings 15th ACM on International conference on multimodal interaction (ICMI), (pp. 467–474).
4. Chen, X., & Koskela, M. (2013). Classification of RGB-D and motion capture sequences using extreme learning machine. Proc. Scandinavian Conference on Image Analysis, 7944, 640–651.
5. Dollar, P., Rabaud, V., Cottrell, G., Belongie, S. (2005). Behavior recognition via sparse spatio-temporal features. In Proceedings Visual Surveillance and Performance Evaluation of Tracking and Surveillance, (pp. 65–72).
6. Ellis, C., Masood, S. Z., Tappen, M. F., Laviola, J. J. Jr., Sukthankar, R. (2013). Exploring the trade-off between accuracy and observational latency in action recognition. International Journal of Computer Vision, 101(3), 420–436.
7. Fengjun, Lv., & Nevatia, R. (2007). Single view human action recognition using key pose matching and viterbi path searching. In Proceedings IEEE Conference Computer Vision and Pattern Recognition (CVPR), (pp. 1–8).
8. Garage, W. (2008). Turtlebot. http://www.willowgarage.com/turtlebot.
9. Han, L., Wu, X., Liang, W., Hou, G., Jia, Y. (2010). Discriminative human action recognition in the learned hierarchical manifold space. Image and Vision Computing, 28(5), 836–849.
10. Huang, G., Zhou, H., Ding, X., Zhang, R. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and CyberneticsPart B Cybernetics, 42(2), 513–529.
11. Huang, P., Hilton, A., Starck, J. (2010). Shape similarity for 3D video sequences of people. International Journal of Computer Vision (IJCV), 89(2–3), 362–381.
12. Ikizler, N., & Duygulu, P. (2009). Histogram of oriented rectangles: A new pose descriptor for human action recognition. Image and Vision Computing, 27(10), 1515–1526.
13. Junejo, I. N., Dexter, E., Laptev, I., Perez, P. (2010). View-independent action recognition from temporal self-similarities. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(1), 172–185.
14. Karali, A., & ElHelw, M. (2012). Motion history of skeletal volumes and temporal change in bounding volume fusion for human action recognition. In Proceedings first international conference on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS), (pp. 79–87).
15. Kilner, J., Guillemaut, J.-Y., Hilton, A. (2009). 3D action matching with key-pose detection. In Proceedings IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), (pp. 1–8).
16. Laptev, I. (2005). On space-time interest points. International Journal of Computer Vision, 64(2–3), 107–123.
17. Laptev, I., Marszalek, M., Schmid, C., Rozefeld, B. (2008). Learning realistic human actions from movies. In Proceedings IEEE Conference Computer Vision and Pattern Recognition (CVPR), (pp. 1–8).
18. Li, W., Zhang, Z., Liu, Z. (2010). Action recognition based on a bag of 3D points. In Proceedings IEEE Conference Computer Vision and Pattern Recognition (CVPR), (pp. 9–14).
19. Lublinerman, R., Ozay, N., Zarpalas, D., Camps, O. (2006). Activity recognition from silhouettes using linear systems, andmodel (in) validation techniques. In Proceedings International Conference on Pattern Recognition (ICPR), (pp. 347–350).
20. Martens, J., & Sutskever, I. (2011). Learning recurrent neural networks with Hessian-free optimization. In Proceedings International Conference on Machine Learning (ICML), (pp. 1033–1040).
21. Oi, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R., Berkeley M. (2013). A comprehensive multimodal human action database. In Proceedings Applications of Computer Vision (WACV), (pp. 53–60).
22. Oliver, N., Garg, A., Horvitz, E. (2004). Layered representations for learning and inferring office activity from multiple sensory channels. Computer Vision and Image Understanding (CVIU), 96(2), 163– 180.
23. Oreifej, O., & Liu, Z. (2013). HON4D: Histogram of oriented 4D normals for activity recognition from depth sequences. In Proceedings IEEE Conference Computer Vision and Pattern Recognition (CVPR), (pp. 716–723).
24. Oreifej, O. (2013). HON4D: Code and Data. http://www.cs.ucf.edu/~oreifej/HON4D.html.
25. Pehlivan, S., & Duygulu, P. (2011). A new pose-based representation for recognizing actions from multiple cameras. Computer Vision and Image Understanding (CVIU), 115(2), 140–151.
26. Quattoni, A., Wang, S., Morency, L., Collins, M., Darrell, T., Csail, M. (2007). Hidden-state conditional random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 29(10), 1848–1852.
27. Ryoo, M.S. (2011). Human activity prediction: Early recognition of ongoing activities from streaming videos. In Proceedings IEEE International Conference on Computer Vision (ICCV), (pp. 1036–1043).
28. Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D. (2005). Conditional models for contextual human motion recognition. In Proceedings International Conference on Computer Vision (ICCV), (pp. 1808–1815).
29. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A. (2011). Real-time human pose recognition in parts from single depth images. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 1297–1304).
30. Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R. (2013). Real-time human pose recognition in parts from single depth images. Communications of the ACM, 56(1), 116–124.
31. Sminchisescu, C., Kanaujia, A., Metaxas, D. (2006). Conditional models for contextual human motion recognition. Computer Vision and Image Understanding (CVIU), 104(2-3), 210–220.
32. Song, Y., Demirdjian, D., Davis, R. (2011). Multi-signal gesture recognition using temporal smoothing hidden conditional random fields. Proceedings Automatic Face and Gesture Recognition (FG), (pp. 388–393).
33. Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O. (2008). Machine recognition of human activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology, 18(11), 1473–1488.
34. Veeraraghavan, A., & Chellappa, R., Roy-Chowdhury, A.K. (2006). The function space of an activity. In Proceedings IEEE Conference. Computer Vision and Pattern Recognition (CVPR), (pp. 959–968).
35. Veeraraghavan, A., Srivastava, A., Roy-Chowdhury, A., Chellappa, R. (2009). Rate-invariant recognition of humans and their activities. IEEE Transactions on Image Processing (TIP), 18(6), 1326–1339.
36. Wang, Y., & Mori, G. (2008). Learning a discriminative hidden part model for human action recognition. Advances in Neural Information Processing Systems (NIPS), 21, 1721–1728.
37. Wang, Y., & Mori, G. (2009). Max-margin hidden conditional random fields for human action recognition. In Proceedings IEEE Conference Computer Vision and Pattern Recognition (CVPR), (pp. 1–8).
38. Wang, J., Liu, Z., Wu, Y., Yuan, J. (2012). Mining Actionlet Ensemble for Action Recognition with Depth Cameras. In Proceedings IEEE Conference Computer Vision and Pattern Recognition (CVPR), (pp. 1290–1297).
39. Wang, L., & Suter, D. (2008). Visual learning and recognition of sequential data manifolds with applications to human movement analysis. Computer Vision and Image Understanding (CVIU), 110(2), 153–172.
40. Wang, H., Ullah, M. M., Klaser, A., Laptev, I., Schmid, C. (2009). Evaluation of local spatio-temporal features for action recognition. In Proceedings British Machine Vision Conference (BMVC), (pp. 1–11).
41. Weinland, D., Grenoble, F., Boyer, E., Ronfard, R., Inc, A. (2007). Action recognition from arbitrary views using 3D exemplars. In Proceedings IEEE International Conference on Computer Vision (ICCV), (pp. 1–7).
42. Xia, L., Chen, C. C., Aggarwal, J. K. (2012). View invariant human action recognition using histograms of 3D joints. In Proceedings CVPR Workshop on Human Activity Understanding from 3D Data (HAU3D), (pp. 20–27).
43. Yang, Y., Hao, A., Zhao, Q. (2008). View-invariant action recognition using interest points. In Proceedings International Conference Multimedia information retrieval, (pp. 305–312).