A lattice-based neuro-computing methodology for real-time human action recognition

Information Sciences

Volumn 181, Issue 10, 2011, Pages 1874-1887

  Syrris, Vassilis ^a   Petridis, Vassilios ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Human action recognition; Lattice theory; Neural networks; PREMONN algorithm; Real time prediction; Time series approximation

Indexed keywords

HUMAN-ACTION RECOGNITION; LATTICE THEORIES; PREMONN ALGORITHM; REAL-TIME PREDICTION; TIME-SERIES APPROXIMATION;

APPROXIMATION ALGORITHMS; COMPUTER VISION; GESTURE RECOGNITION; NEURAL NETWORKS; SIGNAL RECONSTRUCTION;

FEATURE EXTRACTION;

EID: 79952190237     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.05.038     Document Type: Article

References (45)

1. S. Ali, A. Basharat, M. Shah, Chaotic invariants for human action recognition, in: Proceedings of the 11th IEEE International Conference on Computer Vision, 2007, pp. 1-8.
2. H. Alzoubi, and W.D. Pan Fast and accurate global motion estimation algorithm using pixel subsampling Information Sciences 178 17 2008 3415 3425
3. G. Birkhoff, Lattice Theory, third ed., American Mathematical Society, Colloquium Pub. Providence, RI, 1967.
4. A. Bobick, and J. Davis The recognition of human movement using temporal templates IEEE Transactions on Pattern Analysis and Machine Intelligence 23 3 2001 257 267 (Pubitemid 32286632)
5. C. Bregler, Learning and recognizing human dynamics in video sequences, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 1997, pp. 568-574.
6. C. Demonceaux, D. Kachi-Akkouche, Motion detection using wavelet analysis and hierarchical markov models, in: First International Workshop on Spatial Coherence for Visual Motion Analysis, Prague, vol. 3667, 2004, pp. 64-75.
7. P. Dollar, V. Rabaud, G. Cottrell, S. Belongie, Behavior recognition via sparse spatio-temporal feature, in: Proceedings of the Second Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005, pp. 65-72. (Pubitemid 46178025)
8. A.A. Efros, A.C. Berg, G. Mori, J. Malik, Recognizing action at a distance, in: Proceedings of the Ninth IEEE International Conference on Computer Vision, 2003, pp. 726-733.
9. A. Fathi, G. Mori, Action recognition by learning mid-level motion features, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2008, pp. 1-8.
10. A. Gilbert, J. Illingworth, R. Bowden, Scale invariant action recognition using compound features mined from dense spatio-temporal corners, in: Proceedings of the 10th European Conference on Computer Vision: Part I, Lecture Notes in Computer Science, vol. 5302, 2008, pp. 222-233.
11. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri Actions as space-time shapes IEEE Transactions on Pattern Analysis and Machine Intelligence 29 12 2007 2247 2253 Weizmann data set: retrieved June 4, 2008. Available from: < http://www.wisdom.weizmann.ac.il/∼vision/SpaceTimeActions.html > (Pubitemid 350124072)
12. N. Ikizler, P. Duygulu, Human action recognition using distribution of oriented rectangular patches, in: ICCV Workshops on Human Motion, 4814, 2007, pp. 271-284.
13. H. Jiang, M.S. Drew, Li Ze-Nian, Successive convex matching for action detection, in: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2006, pp. 1646-1653. (Pubitemid 44931516)
14. H. Jhuang, T. Serre, L. Wolf, T. Poggio, A biologically inspired system for action recognition, in: Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV), 2007, pp. 1-8.
15. V.G. Kaburlasos, and V. Petridis Fuzzy lattice neurocomputing (FLN) models Neural Networks 13 10 2000 1145 1170
16. V.G. Kaburlasos, Towards a unified modeling and knowledge representation based on lattice theory, in: Computational Intelligence and Soft Computing Applications, Springer, Berlin, 2006.
17. V. Kellokumpu, G. Zhao, M. Pietikäinen, Texture based description of movements for activity analysis, in: Proceedings of the Third International Conference on Computer Vision Theory and Applications, vol. 1, 2008, pp. 206-213.
18. I. Laptev, T. Lindeberg, Local descriptors for spatio-temporal recognition, in: International Workshop on Spatial Coherence for Visual Motion Analysis, vol. 3667, 2004, pp. 91-103. (Pubitemid 44011429)
19. I. Laptev, P. Perez, Retrieving actions in movies, in: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1-8.
20. I. Laptev, M. Marszalek, C. Schmid, B. Rozenfeld, Learning realistic human actions from movies, in: IEEE Computer Vision and Pattern Recognition, 2008, pp. 1-8.
21. Z. Li, Y. Fu, T.S. Huang, S. Yan, Real-time human action recognition by luminance field trajectory analysis, in: Proceedings of the 16th ACM International Conference on Multimedia, 2008, pp. 671-676.
22. C. Liu, W.T. Freeman, E.H. Adelson, Y. Weiss, Human-assisted motion annotation, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
23. A. Madabhushi, J.K. Aggarwal, A Bayesian approach to human activity recognition, in: Proceedings of the Second IEEE Workshop on Visual Surveillance, Fort Collins, 1999, pp. 25-32.
24. H. Meng, M. Freeman, N. Pears, and C. Bailey Real-time human action recognition on an embedded, reconfigurable video processing architecture Journal of Real-Time Image Processing 3 3 2008 163 176
25. J. Mutch, D. Lowe, Multiclass object recognition with sparse, localized features, in: Computer Vision and Pattern Recognition, vol. 1, 2006, pp. 11-18. (Pubitemid 44931307)
26. P. Natarajan, R. Nevatia, Online, real-time tracking and recognition of human actions, in: IEEE Workshop on Motion and Video Computing, 2008, pp. 1-8.
27. J.C. Niebles, L. Fei-Fei, A hierarchical model of shape and appearance for human action classification, in: IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1-8.
28. J.C. Niebles, H. Wang, and L. Fei-Fei Unsupervised learning of human action categories using spatial-temporal words International Journal of Computer Vision 79 3 2008 299 318
29. S. Nowozin, G. Bakir, K. Tsuda, Discriminative subsequence mining for action classification, in: Proceedings of the IEEE International Conference on Computer Vision, 2007, pp. 1919-1923.
30. A. Oikonomopoulos, I. Patras, M. Pantic, Spatiotemporal saliency for human action recognition, in: IEEE International Conference on Multimedia and Expo, 2005, pp. 430-433. (Pubitemid 44668896)
31. A. Oikonomopoulos, I. Patras, M. Pantic, N. Paragios, Trajectory-based representation of human actions, in: Artificial Intelligence for Human Computing, Lecture Notes in Computer Science, vol. 4451, Springer, Berlin, 2007, pp. 133-154.
32. V. Petridis, and A. Kehagias Predictive Modular Neural Networks 1998 Kluwer Academic Publisher Dordrecht
33. C.S. Peirce On the algebra of logic American Journal of Mathematics 3 1880 15 57
34. P. Peursum, H.H. Bui, S. Venkatesh, G. West, Classifying human actions using an incomplete real-time pose skeleton, in: AI Specific Application Areas, Poster Papers, Lecture Notes in Computer Science, vol. 3157, Springer, Berlin, 2004, pp. 971-972.
35. Q. Shi, L. Wang, L. Cheng, A.J. Smola, Discriminative human action segmentation and recognition using semi-Markov model, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8. The Walk-Bend-Draw dataset: retrieved January 28, 2009. Available from: < http://sml.nicta.com. au/∼licheng/aSR/ >.
36. H. Ragheb, S. Velastin, P. Remagnino, T. Ellis, Human action recognition using robust power spectrum features, in: Proceedings of the 15th IEEE International Conference on Image Processing, 2008, pp. 753-756.
37. G. Rigoll, A. Kosmala, M. Schuster, A new approach to video sequence recognition based on statistical methods, in: Proceedings of the International Conference on Image Processing, vol. 3, 1996, pp. 839-842.
38. D.E. Rutherford, Introduction to Lattice Theory, Oliver & Boyd Ltd., Edinburgh, Great Britain, 1965.
39. K. Schindler, L. van Gool, Action snippets: how many frames does human action recognition require? in: IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
40. C. Schuldt, I. Laptev, B. Caputo, Recognizing human actions: a local SVM approach, in: Proceedings of the 17th International Conference on Pattern Recognition, vol. 3, 2004, pp. 32-36. KTH data set: retrieved June 3, 2008. Available from: < http://www.nada.kth.se/cvap/actions/ >.
41. V. Syrris, V. Petridis, Statistical descriptors for human actions classification, in: Proceedings of the 17th Mediterranean Conference on Control and Automation, Thessaloniki, Greece, 2009, pp. 412-415.
42. T. Veit, F. Cao, P. Bouthemy, Probabilistic parameter-free motion detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2004, pp. 715-721.
43. L. Wang, D. Suter, Recognizing human activities from silhouettes: motion subspace and factorial discriminative graphical model, in: IEEE Computer Vision and Pattern Recognition, 2007, pp. 1-8.
44. S.F. Wong, R. Cipolla, Extracting spatio-temporal interest points using global information, in: Proceedings of the IEEE International Conference on Computer Vision, 2007, pp. 1-8.
45. C. Yeo, P. Ahammad, K. Ramachandran, S. Shankar Sastry, Compressed domain real-time action recognition, in: IEEE Proceedings of the Eighth Workshop on Multimedia Signal Processing, 2006, pp. 33-36.