Movement pattern histogram for action recognition and retrieval

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

Volumn 8690 LNCS, Issue PART 2, 2014, Pages 695-710

  Ciptadi, Arridhana ^a   Goodwin, Matthew S ^b   Rehg, James M ^a  

^a Georgia Institute of Technology   (United States)

^b NORTHEASTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENCODING (SYMBOLS); STATISTICAL METHODS;

ACTION RECOGNITION; ACTION REPRESENTATIONS; ACTION RETRIEVALS; CHILDREN WITH AUTISMS; CLASSROOM SETTINGS; MAXIMUM MATCHINGS; MOVEMENT PATTERN; TEMPORAL DYNAMICS;

GRAPHIC METHODS;

EID: 84906508322     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10605-2_45     Document Type: Conference Paper

References (31)

1. Albinali, F., Goodwin, M.S., Intille, S.S.: Recognizing stereotypical motor movements in the laboratory and classroom: A case study with children on the aautism spectrum. In: UbiComp (2009)
2. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TR, 4th edn. Text Revision. American Psychiatric Pub. (2000)
3. Cao, L., Ji, R., Gao, Y., Liu, W., Tian, Q.: Mining spatiotemporal video patterns towards robust action retrieval. Neurocomputing (2012)
4. Chaudhry, R., Ravichandran, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: CVPR (2009)
5. Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior Recognition via Sparse Spatio-Temporal Features. ICCV-VS PETS (2005)
6. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes. PAMI (2007)
7. Grundmann, M., Kwatra, V., Essa, I.: Auto-directed video stabilization with robust l1 optimal camera paths. In: CVPR (2011)
8. Jain, M., Jégou, H., Bouthemy, P., et al.: Better exploiting motion for better action recognition. In: CVPR (2013)
9. Johansson, G.: Visual Perception of Biological Motion and a Model for Its Analysis. Attention, Perception, & Psychophysics 14(2), 201-211 (1973)
10. Junejo, I., Dexter, E., Laptev, I., Pérez, P.: View-Independent Action Recognition from Temporal Self-Similarities. PAMI (2010)
11. Ke, Y., Sukthankar, R., Hebert, M.: Volumetric features for video event detection. IJCV (2010)
12. Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-time imaging 11(3), 172-185 (2005)
13. Kliper-Gross, O., Gurovich, Y., Hassner, T.,Wolf, L.: Motion interchange patterns for action recognition in unconstrained videos. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 256-269. Springer, Heidelberg (2012)
14. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning Realistic Human Actions from Movies. In: CVPR (2008)
15. Li, B., Camps, O.I., Sznaier, M.: Cross-view activity recognition using hankelets. In: CVPR (2012)
16. Li, R., Zickler, T.: Discriminative Virtual Views for Cross-View Action Recognition. In: CVPR (2012)
17. Liu, J., Shah,M., Kuipers, B., Savarese, S.: Cross-View Action Recognition via View Knowledge Transfer. In: CVPR (2011)
18. Messing, R., Pal, C., Kautz, H.: Activity Recognition Using the Velocity Histories of Tracked Keypoints. In: ICCV (2009)
19. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143-156. Springer, Heidelberg (2010)
20. Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976-990 (2010)
21. Reddy, K.K., Shah, M.: Recognizing 50 human action categories of web videos. Machine Vision and Applications (2012)
22. Sadanand, S., Corso, J.J.: Action bank: A high-level representation of activity in video. In: CVPR (2012)
23. Shi, J., Tomasi, C.: Good features to track. In: CVPR (1994)
24. Solmaz, B., Assari, S.M., Shah, M.: Classifying web videos using a global video descriptor. Machine Vision and Applications (2012)
25. Tran, D., Sorokin, A.: Human Activity Recognition with Metric Learning. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 548-561. Springer, Heidelberg (2008)
26. Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. In: IJCV (2013)
27. Weinland, D., Boyer, E., Ronfard, R.: Action Recognition from Arbitrary Views using 3D Exemplars. IJCV (2007)
28. Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic Huber-L1 optical flow. BMVC (2009)
29. Yuan, C., Li, X., Hu, W., Ling, H., Maybank, S.: 3d r transform on spatio-temporal interest points for action recognition. In: CVPR (2013)
30. Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S., Shi, C.: Cross-view action recognition via a continuous virtual path. In: CVPR (2013)
31. Zhou, F., de la Torre, F.: Canonical Time Warping for Alignment of Human Behavior. NIPS (2009)