3D action classification using sparse spatio-temporal feature representations

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7432 LNCS, Issue PART 2, 2012, Pages 166-175

  Azary, Sherif ^a   Savakis, Andreas ^a  

^a ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

3D VIDEO; BACKGROUND CLUTTER; CLASSIFICATION ACCURACY; DATA SETS; DEPTH INFORMATION; DEPTH SENSORS; DESCRIPTORS; FEATURE REPRESENTATION; HUMAN MOTIONS; KINEMATIC JOINTS; LEAST SQUARE; MICROSOFT RESEARCHES; OVER-COMPLETE; REGULARIZATION PARAMETERS; SPARSE REPRESENTATION; SPATIO-TEMPORAL;

KINEMATICS;

THREE DIMENSIONAL COMPUTER GRAPHICS;

EID: 84866722492     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33191-6_17     Document Type: Conference Paper

References (33)

1. Ghasemzadeh, H., Loseu, V., Jafari, R.: Collaborative Signal Processing for Action Recognition in Body Sensor Networks: A Distributed Classification Algorithm Using Mo-tion Transcripts. In: Proc. 9th ACM/IEEE Int. Conf. Inf. Process. (2010)
2. Raja, K., Laptev, I., Perez, P., Oisel, L.: Joint pose estimation and action recognition in image graphs. In: 18th IEEE International Conference on International Conference on Image Processing, ICIP (2011)
3. Weinland, D., Boyer, E., Ronfard, R.: Action Recognition from Arbitrary Views using 3D Exemplars. In: IEEE ICCV (2007)
4. Maji, S., Bourdev, L., Malik, J.: Action recognition from a distributed representation of pose and appearance. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2011)
5. Wang, Y., Zhang, Z.: View-invariant action recognition in surveillance videos. In: First Asian Conference on Pattern Recognition, ACPR (2011)
6. Imtiaz, H., Mahbub, U., Ahad, M.A.R.: Action recognition algorithm based on optical flow and RANSAC in frequency domain. In: Proceedings of SICE Annual Conference, SICE (2011)
7. Ahad, M.A.R., Tan, J., Kim, H., Ishikawa, S.: Action recognition by employing combined directional motion history and energy images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW (2010)
8. Lopes, A. P. B., Oliveira, R. S., de Almeida, J. M., de A Araujo, A.: Comparing alternatives for capturing dynamic information in Bag-of-Visual- Features approaches applied to human actions recognition. In: IEEE International Workshop on Multimedia Signal Processing, MMSP (2009)
9. Liu, J., Yang, J., Zhang, Y., He, X.: Action Recognition by Multiple Features and Hyper-Sphere Multi-class SVM. In: 20th International Conference on Pattern Recognition, ICPR (2010)
10. Ji, X., Liu, H., Li, Y.: Human actions recognition using Fuzzy PCA and discriminative hidden model. In: IEEE International Conference on Fuzzy Systems, FUZZ (2010)
11. Azary, S., Savakis, A.: View Invariant Activity Recognition with Manifold Learning. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammound, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6454, pp. 606-615. Springer, Heidelberg (2010)
12. Junejo, I.N., Dexter, E., Laptev, I., Perez, P.: View-Independent Action Recognition from Temporal Self-Similarities. IEEE Transactions on Pattern Analysis and Machine Intelligence (2011)
13. Gall, J., Yao, A., Razavi, N., Gool, L.V., Lempitsky, V.: Hough Forests for Object Detection, Tracking, and Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2011)
14. Laptev, I.: On Space-Time Interest Points. International Journal of Computer Vision (2005)
15. Willems, G., Tuytelaars, T., Van Gool, L.: An efficient dense and scale-invariant spatio-temporal interest point detector. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 650-663. Springer, Heidelberg (2008)
16. Juran, J.M.: The non-Pareto Principle: Mea culpa. Quality Progress, 8-9 (May 1975)
17. Farmer, J.D., Geanakoplos, J.: Power laws in economics and elsewhere. Santa Fe Institute, Santa Fe (2008)
18. West, G.B.: The Origin of Universal Scaling Laws in Biology. Oxford University Press, New York (1999)
19. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: IMC 2007 Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, New York, NY (2007)
20. Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust Face Recognition via Sparse Representation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI (2009)
21. Qiu, F., Xu, Y., Wang, C., Yang, Y.: Noisy image super-resolution with sparse mixing estimators. In: 4th International Congress on Image and Signal Processing, CISP (2011)
22. Bao, L., Liu, W., Zhu, Y., Pu, Z, Magnin: Sparse representation based MRI denoising with total variation. In: 9th International Conference on Signal Processing, ICSP (2008)
23. Zuo, Y., Zhang, B.: General image classification based on sparse representation. In: 9th IEEE International Conference on Cognitive Informatics, ICCI (2010)
24. Zhang, J., Wang, Y., Chen, J., Li, Q.: Sparse Representation for Action Recognition. In: 3rd International Congress on Image and Signal Processing, CISP 2010 (2010)
25. Liu, C., Yang, Y., Chen, Y.: Human Action Recognition using Sparse Representation. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS (2009)
26. 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 (2011)
27. Wright, J., Ma, Y., Maira, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse Representation for Computer Vision and Pattern Recognition. Proceedings of the IEEE 98(6), 1031-1044 (2010)
28. Miller, S.J.: The Method of Least Squares. Mathematics Department Brown University, Providence, RI (2006)
29. Bektaş, S., Şişman, Y.: The comparison of L1 and L2-norm minimization methods. International Journal of the Physical Sciences, IJPS (2010)
30. Donoho, D.L., Elad, M., Temlyakov, V.: Stable recovery of sparse overcomplete representations. IEEE Transactions on Information Theory (2005)
31. Donoho, D.L., Tsaig, Y.: Fast Solution of L1-norm Minimization Problems When the Solution May Be Sparse, Stanford CA, 94305, Department of Statistics, Stanford University (2006)
32. Schmidt, M.: Least Squares Optimization with L1-Norm Regularization. University of British Columbia (2005)
33. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: CVPR Workshop, San Fransisco, CA (June 2010)