Action recognition using 3D DAISY descriptor

Machine Vision and Applications

Volumn 25, Issue 1, 2014, Pages 159-171

  Cao, Xiaochun ^a,b   Zhang, Hua ^a   Deng, Chao ^a   Liu, Qiguang ^c   Liu, Hanyu ^d  

^a TIANJIN UNIVERSITY   (China)

^b University of Chinese Academy of Sciences   (China)

^c UNIVERSITY OF ROCHESTER   (United States)

^d STONY BROOK UNIVERSITY   (United States)

Author keywords

3D DAISY descriptor; Action recognition

Indexed keywords

EID: 84894902501     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-013-0545-6     Document Type: Article

References (41)

1. Aggarwal, J.K.; Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. (CSUR) 43(3) (2011)
2. Ali, S. Basharat, A.; Shah, M.: Chaotic invariants for human action recognition. In: ICCV (2007)
3. Bay, H.; Ess, A.; Tuytelaars, T.; Gool, L.V.: SURF: speeded up robust features. CVIU 110(3), 346-359 (2008)
4. Bregonzio, M.; Gong, S.; Xiang, T.: Recognising action as clouds of space-time interest points, CVPR (2009)
5. Chang, C.-C.; Lin, C.-J.: LIBSVM: a library for support vector machines (2001)
6. Chen, C.-C.; Aggarwal, K.K.: Recognizing human action from a far field of view. IEEE workshop on motion and video computing (WMVC) (2009)
7. Deng, C.; Cao, X.; Liu, H.; Chen. J.: A global spatio-temporal representation for action recognition. In: ICPR, Istanbul, pp. 1816-1819 (2010)
8. Dollár, P.; Rabaud, V.; Cottrell, G.; Belongie S.: Behavior recognition via sparse spatio-temporal features. In: VS-PETS, pp. 65-72 (2005)
9. Efros, A.A.; Berg, A.C.; Mori, G.; Malik, J.: Recognizing action at a distance. In: ICCV, Nice, pp. 726-733 (2003)
10. Fathi, A.; Mori, G.: Action recognition by learning mid-level motion features. In: CVPR, Anchorage, (2008)
11. Gorelick, L.; Blank, M.; Shechtman, E.; Irani, M.; Basri, R.: Actions as space-time shapes. PAMI 29(12), 2247-2253 (2007)
12. Jhuang, H.; Serre, T.; Wolf, L.; Poggio, T.: A biologically inspired system for action recognition. IEEE international conference on computer vision (ICCV) (2007)
13. Ke, Y.; Sukthankar, R.; Hebert, M.: Efficient visual event detection using volumetric features. In: ICCV, pp. 166-173 (2005)
14. Kläser, A.; Marszałek, M.; Schmid. C.: A spatio-temporal descriptor based on 3D-gradients. In: BMVC, pp. 995-1004 (2008)
15. Kovashka, A.; Grauman, K.: Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In: CVPR, San Francisco (2010)
16. Laptev, I.; Marszałek, M.; Schmid, C.; Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR, Anchorage (2008)
17. Laptev, I.: On space-time interest points. IJCV 64(2), 107-123 (2005)
18. Lin, Z.; Jiang, Z.; Davis, L.S.: Recognizing actions by shape-motion prototype trees. In: ICCV, Kyoto (2009)
19. Liu, J.; Luo, J.; Shah, M.: Recognizing realistic actions from videos "in the wild". In: CVPR, Miami, pp. 1996-2003 (2009)
20. Liu, J.; Shah, M.: Learning human actions via information maximization. In: CVPR, Anchorage (2008)
21. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91-110 (2004)
22. Marszalek, M.; Laptev, I.; Schmid, C.: Actions in context. In: CVPR, Miami (2009)
23. Messing, R.; Pal, C.; Kautz, H.: Activity recognition using the velocity histories of tracked keypoints. In: ICCV, Kyoto (2009)
24. Mikolajczyk, K.; Schmid, C.: A performance evaluation of local descriptors. PAMI 27(10), 1615-1630 (2005)
25. Niebles, J.C.; Wang, H.; Li, F.-F.: Unsupervised learning of human action categories using spatial-temporal words. IJCV 79(3), 299-318 (2008)
26. Nowozin, S.; Bakir, G.; Tsuda K.: Discriminative subsequence mining for action classification. In: ICCV, pp. 1919-1923 (2007)
27. Rodriguez, M.D.; Ahmed, J.; Shah, M.: Action MACH a spatio-temporal maximum average correlation height filter for action recognition. In: CVPR, Anchorage (2008)
28. Ryoo, M.S.; Aggarwal, J.K.: Spatio-temporal relationship match: video structure comparison for recognition of complex human activities. In: ICCV, Kyoto, pp. 1593-1600 (2009)
29. Ryoo, M.S.; Aggarwal, J.K.: UT-interaction dataset, ICPR contest on semantic description of human activities (SDHA) (2010)
30. Schüldt, C.; Laptev, I. Caputo, B.: Recognizing human actions: a local SVM approach. In: ICPR, pp. 32-36 (2004)
31. Scovanner, P.; Ali, S.; Shah, M.: A 3-dimensional SIFT descriptor and its application to action recognition. In: ACM multimedia, pp. 357-360 (2007)
32. Sun, J.; Wu, X.; Yan, S.; Cheong, L.-F.; Chua, T.-S.; Li, J.: Hierarchical spatio-temporal context modeling for action recognition. In: CVPR, pp. 2004-2011 (2009)
33. Tola, E.; Lepetit, V.; Fua, P.: A fast local descriptor for dense matching. In: CVPR, Anchorage (2008)
34. Waltisberg, D.; Yao, A.; Gall, J.; Gool, L.V.: Variations of a Hough-voting action recognition system. Recognizing Patterns in Signals, Speech, Images and Videos, LNCS, vol. 6388, (2010)
35. Wang, H.; Ullah, M.M.; Kläser, A.; Laptev, I.; Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC, London (2009)
36. Weinland, D.; Ronfard, R.; Boyer, E.: Free viewpoint action recognition using motion history volumes. CVIU 104(2-3), 249-257 (2006)
37. Willems, G.; Tuytelaars, T.; Gool, L.V.: An efficient dense and scale-invariant spatio-temporal interest point detector. ECCV 2, 650-663 (2008)
38. Winder, S.; Hua, G.; Brown, M.: Picking the best daisy. In: CVPR, pp. 178-185 (2009)
39. Yilmaz, A. Shah, M.: Actions sketch: a novel action representation. In: CVPR, pp. 984-989 (2005)
40. Zhang, Z.; Hu, Y.; Chan, S.; Chia, L.-T.: Motion context: a new representation for human action recognition. In: ECCV, pp. 817-829 (2008)
41. Zhang, J.; Marszałek, M.; Lazebnik, S.; Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. IJCV 73(2), 213-238 (2007)