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Volumn , Issue , 2013, Pages 2595-2602

Sampling strategies for real-time action recognition

Author keywords

[No Author keywords available]

Indexed keywords

ACTION RECOGNITION SYSTEMS; BETTER PERFORMANCE; HISTOGRAM INTERSECTION KERNELS; INTEREST POINT DETECTORS; RANDOM SAMPLING METHOD; REAL-WORLD DATASETS; SAMPLING STRATEGIES; SPATIO-TEMPORAL FEATURES;

EID: 84887327356     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.335     Document Type: Conference Paper
Times cited : (103)

References (37)
  • 1
    • 34648839288 scopus 로고    scopus 로고
    • Hyperfeatures-multilevel local coding for visual recognition
    • 2
    • A. Agarwal and B. Triggs. Hyperfeatures-multilevel local coding for visual recognition. In ECCV, 2006. 2
    • (2006) ECCV
    • Agarwal, A.1    Triggs, B.2
  • 2
    • 84898422360 scopus 로고    scopus 로고
    • A probabilistic framework for recognizing similar actions using spatio-temporal features
    • 2
    • I. R. Alonso Patron-perez. A probabilistic framework for recognizing similar actions using spatio-temporal features. In BMVC, 2007. 2
    • (2007) BMVC
    • Alonso Patron-Perez, I.R.1
  • 5
    • 51949101231 scopus 로고    scopus 로고
    • A discriminatively trained, multiscale, deformable part model
    • 2
    • P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In CVPR, pages 1-8, 2008. 2
    • (2008) CVPR , pp. 1-8
    • Felzenszwalb, P.1    McAllester, D.2    Ramanan, D.3
  • 6
    • 84867849524 scopus 로고    scopus 로고
    • Trajectory-based modeling of human actions with motion reference points
    • 7
    • Y.-G. Jiang, Q. Dai, X. Xue, W. Liu, and C.-W. Ngo. Trajectory-based modeling of human actions with motion reference points. In ECCV, pages 425-438, 2012. 7
    • (2012) ECCV , pp. 425-438
    • Jiang, Y.-G.1    Dai, Q.2    Xue, X.3    Liu, W.4    Ngo, C.-W.5
  • 7
    • 33745943239 scopus 로고    scopus 로고
    • Efficient visual event detection using volumetric features
    • 2, 3
    • Y. Ke, R. Sukthankar, and M. Hebert. Efficient visual event detection using volumetric features. In ICCV, volume 1, pages 166-173, 2005. 2, 3
    • (2005) ICCV , vol.1 , pp. 166-173
    • Ke, Y.1    Sukthankar, R.2    Hebert, M.3
  • 8
    • 84867849228 scopus 로고    scopus 로고
    • Motion interchange patterns for action recognition in unconstrained videos
    • 7
    • O. Kliper-Gross, Y. Gurovich, T. Hassner, and L. Wolf. Motion interchange patterns for action recognition in unconstrained videos. In ECCV, pages 256-269, 2012. 7
    • (2012) ECCV , pp. 256-269
    • Kliper-Gross, O.1    Gurovich, Y.2    Hassner, T.3    Wolf, L.4
  • 9
    • 84898426452 scopus 로고    scopus 로고
    • A spatio-temporal descriptor based on 3d-gradients
    • 3
    • A. Klser, M. Marsza?ek, and C. Schmid. A spatio-temporal descriptor based on 3d-gradients. In BMVC, pages 995-1004, 2008. 3
    • (2008) BMVC , pp. 995-1004
    • Klser, A.1    Marszaek, M.2    Schmid, C.3
  • 10
    • 84856682691 scopus 로고    scopus 로고
    • Hmdb: A large video database for human motion recognition
    • 1, 4, 7
    • H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. Hmdb: A large video database for human motion recognition. In ICCV, pages 2556-2563, 2011. 1, 4, 7
    • (2011) ICCV , pp. 2556-2563
    • Kuehne, H.1    Jhuang, H.2    Garrote, E.3    Poggio, T.4    Serre, T.5
  • 11
    • 51949099868 scopus 로고    scopus 로고
    • Beyond sliding windows: Object localization by efficient subwindow search
    • IEEE 3
    • C. H. Lampert, M. B. Blaschko, and T. Hofmann. Beyond sliding windows: Object localization by efficient subwindow search. In CVPR, pages 1-8. IEEE, 2008. 3
    • (2008) CVPR , pp. 1-8
    • Lampert, C.H.1    Blaschko, M.B.2    Hofmann, T.3
  • 13
    • 51949083365 scopus 로고    scopus 로고
    • Learning realistic human actions from movies
    • 3, 6
    • I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, pages 1-8, 2008. 3, 6
    • (2008) CVPR , pp. 1-8
    • Laptev, I.1    Marszalek, M.2    Schmid, C.3    Rozenfeld, B.4
  • 14
    • 80052874098 scopus 로고    scopus 로고
    • Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
    • 1
    • Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In CVPR, pages 3361-3368, 2011. 1
    • (2011) CVPR , pp. 3361-3368
    • Le, Q.V.1    Zou, W.Y.2    Yeung, S.Y.3    Ng, A.Y.4
  • 15
    • 0035358496 scopus 로고    scopus 로고
    • Representing and recognizing the visual appearance of materials using three-dimensional textons
    • 2
    • T. Leung and J. Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43:2944, 2001. 2
    • (2001) International Journal of Computer Vision , vol.43 , pp. 2944
    • Leung, T.1    Malik, J.2
  • 16
    • 84875269302 scopus 로고    scopus 로고
    • Human action recognition based on boosted feature selection and naive bayes nearestneighbor classification
    • 2
    • L. Liu, L. Shao, and P. Rockett. Human action recognition based on boosted feature selection and naive bayes nearestneighbor classification. Signal Processing, pages 1521-1530, 2012. 2
    • (2012) Signal Processing , pp. 1521-1530
    • Liu, L.1    Shao, L.2    Rockett, P.3
  • 17
    • 51949098112 scopus 로고    scopus 로고
    • Classification using intersection kernel support vector machines is efficient
    • IEEE 4
    • S. Maji, A. C. Berg, and J. Malik. Classification using intersection kernel support vector machines is efficient. In CVPR, pages 1-8. IEEE, 2008. 4
    • (2008) CVPR , pp. 1-8
    • Maji, S.1    Berg, A.C.2    Malik, J.3
  • 18
    • 70450177757 scopus 로고    scopus 로고
    • Actions in context
    • 1
    • M. Marszalek, I. Laptev, and C. Schmid. Actions in context. In CVPR, pages 2929-2936, 2009. 1
    • (2009) CVPR , pp. 2929-2936
    • Marszalek, M.1    Laptev, I.2    Schmid, C.3
  • 19
    • 84867848624 scopus 로고    scopus 로고
    • Dynamic eye movement datasets and learnt saliency models for visual action recognition
    • 2
    • S. Mathe and C. Sminchisescu. Dynamic eye movement datasets and learnt saliency models for visual action recognition. In ECCV, pages 842-856, 2012. 2
    • (2012) ECCV , pp. 842-856
    • Mathe, S.1    Sminchisescu, C.2
  • 21
    • 33745835368 scopus 로고    scopus 로고
    • Sampling strategies for bag-of-features image classification
    • 1, 2, 3, 5
    • E. Nowak, F. Jurie, and B. Triggs. Sampling strategies for bag-of-features image classification. In ECCV (4), pages 490-503, 2006. 1, 2, 3, 5
    • (2006) ECCV , Issue.4 , pp. 490-503
    • Nowak, E.1    Jurie, F.2    Triggs, B.3
  • 22
    • 84879550059 scopus 로고    scopus 로고
    • Recognizing 50 human action categories of web videos
    • September. 1, 4, 7
    • K. K. Reddy and M. Shah. Recognizing 50 human action categories of web videos. Machine Vision and Applications Journal, pages 1-11, September, 2012. 1, 4, 7
    • (2012) Machine Vision and Applications Journal , pp. 1-11
    • Reddy, K.K.1    Shah, M.2
  • 23
    • 84866718894 scopus 로고    scopus 로고
    • Action bank: A high-level representation of activity in video
    • 1, 6, 7
    • S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. In CVPR, pages 1234-1241, 2012. 1, 6, 7
    • (2012) CVPR , pp. 1234-1241
    • Sadanand, S.1    Corso, J.J.2
  • 24
    • 84887336802 scopus 로고    scopus 로고
    • Learning discriminative space-time actions from weakly labelled videos
    • 1, 7
    • M. Sapienza, F. Cuzzolin, and P. H. T. and. Learning discriminative space-time actions from weakly labelled videos. In ECCV, 2012. 1, 7
    • (2012) ECCV
    • Sapienza, M.1    Cuzzolin, F.2
  • 25
    • 10044233701 scopus 로고    scopus 로고
    • Recognizing human actions: A local svm approach
    • 1, 4, 6
    • C. Schüldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach. In ICPR (3), pages 32-36, 2004. 1, 4, 6
    • (2004) ICPR , Issue.3 , pp. 32-36
    • Schüldt, C.1    Laptev, I.2    Caputo, B.3
  • 29
    • 84867884391 scopus 로고    scopus 로고
    • Space-variant descriptor sampling for action recognition based on saliency and eye movements
    • 2, 4
    • E. Vig, M. Dorr, and D. Cox. Space-variant descriptor sampling for action recognition based on saliency and eye movements. In ECCV, pages 84-97, 2012. 2, 4
    • (2012) ECCV , pp. 84-97
    • Vig, E.1    Dorr, M.2    Cox, D.3
  • 30
    • 80052877143 scopus 로고    scopus 로고
    • Action recognition by dense trajectories
    • 1, 2, 3, 4
    • H. Wang, A. Klaser, C. Schmid, and C.-L. Liu. Action recognition by dense trajectories. In CVPR, pages 3169-3176, 2011. 1, 2, 3, 4
    • (2011) CVPR , pp. 3169-3176
    • Wang, H.1    Klaser, A.2    Schmid, C.3    Liu, C.-L.4
  • 31
    • 84887394800 scopus 로고    scopus 로고
    • Dense trajectories and motion boundary descriptors for action recognition
    • Technical report. 4, 6, 7
    • H. Wang, A. Klaser, C. Schmid, and C.-L. Liu. Dense trajectories and motion boundary descriptors for action recognition. Technical report, INRIA, 2012. 4, 6, 7
    • (2012) INRIA
    • Wang, H.1    Klaser, A.2    Schmid, C.3    Liu, C.-L.4
  • 32
    • 84898890371 scopus 로고    scopus 로고
    • Evaluation of local spatio-temporal features for action recognition
    • 1, 2, 3, 4, 6
    • H. Wang, M. M. Ullah, A. Klser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. In BMVC, pages 127-137, 2009. 1, 2, 3, 4, 6
    • (2009) BMVC , pp. 127-137
    • Wang, H.1    Ullah, M.M.2    Klser, A.3    Laptev, I.4    Schmid, C.5
  • 33
    • 56749155587 scopus 로고    scopus 로고
    • An efficient dense and scale-invariant spatio-temporal interest point detector
    • 1, 2, 3, 6
    • G. Willems, T. Tuytelaars, and L. J. V. Gool. An efficient dense and scale-invariant spatio-temporal interest point detector. In ECCV, pages 650-663, 2008. 1, 2, 3, 6
    • (2008) ECCV , pp. 650-663
    • Willems, G.1    Tuytelaars, T.2    Gool, L.J.V.3
  • 35
    • 77953205594 scopus 로고    scopus 로고
    • Local trinary patterns for human action recognition
    • 2
    • L. Yeffet and L. Wolf. Local trinary patterns for human action recognition. In ICCV, pages 492-497, 2009. 2
    • (2009) ICCV , pp. 492-497
    • Yeffet, L.1    Wolf, L.2
  • 36
    • 84898414417 scopus 로고    scopus 로고
    • Real-time action recognition by spatiotemporal semantic and structural forests
    • 2
    • T.-H. Yu, T.-K. Kim, and R. Cipolla. Real-time action recognition by spatiotemporal semantic and structural forests. In BMVC, pages 1-12, 2010. 2
    • (2010) BMVC , pp. 1-12
    • Yu, T.-H.1    Kim, T.-K.2    Cipolla, R.3
  • 37
    • 33846580425 scopus 로고    scopus 로고
    • Local features and kernels for classification of texture and object categories: A comprehensive study
    • 4, 6
    • J. Zhang, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: a comprehensive study. International Journal of Computer Vision, 73:2007, 2007. 4, 6
    • (2007) International Journal of Computer Vision , vol.73 , pp. 2007
    • Zhang, J.1    Lazebnik, S.2    Schmid, C.3


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