메뉴 건너뛰기




Volumn , Issue , 2013, Pages 3551-3558

Action recognition with improved trajectories

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; OPTICAL FLOWS; TRAJECTORIES;

EID: 84898805910     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.441     Document Type: Conference Paper
Times cited : (3454)

References (43)
  • 1
    • 79955649703 scopus 로고    scopus 로고
    • Human activity analysis: A review
    • J. Aggarwal and M. Ryoo. Human activity analysis: A review. ACM Computing Surveys, 43(3):16:1-16:43, 2011.
    • (2011) ACM Computing Surveys , vol.43 , Issue.3 , pp. 161-1643
    • Aggarwal, J.1    Ryoo, M.2
  • 2
    • 84866678025 scopus 로고    scopus 로고
    • Three things everyone should know to improve object retrieval
    • R. Arandjelovic and A. Zisserman. Three things everyone should know to improve object retrieval. In CVPR, pages 2911-2918, 2012.
    • (2012) CVPR , pp. 2911-2918
    • Arandjelovic, R.1    Zisserman, A.2
  • 3
  • 4
    • 84856661125 scopus 로고    scopus 로고
    • Learning spatiotemporal graphs of human activities
    • W. Brendel and S. Todorovic. Learning spatiotemporal graphs of human activities. In ICCV, 2011.
    • (2011) ICCV
    • Brendel, W.1    Todorovic, S.2
  • 5
    • 84898420173 scopus 로고    scopus 로고
    • The devil is in the details: An evaluation of recent feature encoding methods
    • K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman. The devil is in the details: an evaluation of recent feature encoding methods. In BMVC, 2011.
    • (2011) BMVC
    • Chatfield, K.1    Lempitsky, V.2    Vedaldi, A.3    Zisserman, A.4
  • 6
    • 34948855444 scopus 로고    scopus 로고
    • Human detection using oriented histograms of flow and appearance
    • N. Dalal, B. Triggs, and C. Schmid. Human detection using oriented histograms of flow and appearance. In ECCV, 2006.
    • (2006) ECCV
    • Dalal, N.1    Triggs, B.2    Schmid, C.3
  • 8
    • 84975278151 scopus 로고    scopus 로고
    • Two-frame motion estimation based on polynomial expansion
    • G. Farneb?ack. Two-frame motion estimation based on polynomial expansion. In SCIA, 2003.
    • (2003) SCIA
    • Farneback, G.1
  • 9
    • 77955422240 scopus 로고    scopus 로고
    • Object detection with discriminatively trained part-based models
    • P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. IEEE PAMI, 32(9):1627-1645, 2010.
    • (2010) IEEE PAMI , vol.32 , Issue.9 , pp. 1627-1645
    • Felzenszwalb, P.F.1    Girshick, R.B.2    McAllester, D.3    Ramanan, D.4
  • 10
    • 51949088307 scopus 로고    scopus 로고
    • Progressive search space reduction for human pose estimation
    • V. Ferrari, M. Marin-Jimenez, and A. Zisserman. Progressive search space reduction for human pose estimation. In CVPR, 2008.
    • (2008) CVPR
    • Ferrari, V.1    Marin-Jimenez, M.2    Zisserman, A.3
  • 11
    • 0019574599 scopus 로고
    • Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
    • M. A. Fischler and R. C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981.
    • (1981) Communications of the ACM , vol.24 , Issue.6 , pp. 381-395
    • Fischler, M.A.1    Bolles, R.C.2
  • 12
    • 84898405815 scopus 로고    scopus 로고
    • Recognizing activities with cluster-trees of tracklets
    • A. Gaidon, Z. Harchaoui, and C. Schmid. Recognizing activities with cluster-trees of tracklets. In BMVC, 2012.
    • (2012) BMVC
    • Gaidon, A.1    Harchaoui, Z.2    Schmid, C.3
  • 13
    • 79960215552 scopus 로고    scopus 로고
    • Evaluation of interest point detectors and feature descriptors for visual tracking
    • S. Gauglitz, T. H?ollerer, and M. Turk. Evaluation of interest point detectors and feature descriptors for visual tracking. IJCV, 94(3):335-360, 2011.
    • (2011) IJCV , vol.94 , Issue.3 , pp. 335-360
    • Gauglitz, S.1    Hollerer, T.2    Turk, M.3
  • 14
    • 84887398298 scopus 로고    scopus 로고
    • Better exploiting motion for better action recognition
    • M. Jain, H. J?egou, and P. Bouthemy. Better exploiting motion for better action recognition. In CVPR, 2013.
    • (2013) CVPR
    • Jain, M.1    Jegou, H.2    Bouthemy, P.3
  • 15
    • 84877645596 scopus 로고    scopus 로고
    • Trajectorybased modeling of human actions with motion reference points
    • Y.-G. Jiang, Q. Dai, X. Xue, W. Liu, and C.-W. Ngo. Trajectorybased modeling of human actions with motion reference points. In ECCV, 2012.
    • (2012) ECCV
    • Jiang, Y.-G.1    Dai, Q.2    Xue, X.3    Liu, W.4    Ngo, C.-W.5
  • 16
    • 84898426452 scopus 로고    scopus 로고
    • A spatio-temporal descriptor based on 3D-gradients
    • A. Kl?aser, M. Marszałek, and C. Schmid. A spatio-temporal descriptor based on 3D-gradients. In BMVC, 2008.
    • (2008) BMVC
    • Klaser, A.1
  • 17
    • 84883368489 scopus 로고    scopus 로고
    • Motion interchange patterns for action recognition in unconstrained videos
    • O. Kliper-Gross, Y. Gurovich, T. Hassner, and L. Wolf. Motion interchange patterns for action recognition in unconstrained videos. In ECCV, 2012.
    • (2012) ECCV
    • Kliper-Gross, O.1    Gurovich, Y.2    Hassner, T.3    Wolf, L.4
  • 18
    • 84856682691 scopus 로고    scopus 로고
    • HMDB: A large video database for human motion recognition
    • 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.
    • (2011) ICCV , pp. 2556-2563
    • Kuehne, H.1    Jhuang, H.2    Garrote, E.3    Poggio, T.4    Serre, T.5
  • 19
    • 24944451092 scopus 로고    scopus 로고
    • On space-time interest points
    • I. Laptev. On space-time interest points. IJCV, 64(2-3):107-123, 2005.
    • (2005) IJCV , vol.64 , Issue.2-3 , pp. 107-123
    • Laptev, I.1
  • 21
    • 70450203660 scopus 로고    scopus 로고
    • Recognizing realistic actions from videos in the wild
    • J. Liu, J. Luo, and M. Shah. Recognizing realistic actions from videos in the wild. In CVPR, 2009.
    • (2009) CVPR
    • Liu, J.1    Luo, J.2    Shah, M.3
  • 23
    • 84893353862 scopus 로고    scopus 로고
    • Dynamic eye movement datasets and learnt saliency models for visual action recognition
    • S. Mathe and C. Sminchisescu. Dynamic eye movement datasets and learnt saliency models for visual action recognition. In ECCV, 2012.
    • (2012) ECCV
    • Mathe, S.1    Sminchisescu, C.2
  • 24
    • 80052874353 scopus 로고    scopus 로고
    • Modeling temporal structure of decomposable motion segments for activity classification
    • J. C. Niebles, C.-W. Chen, and L. Fei-Fei. Modeling temporal structure of decomposable motion segments for activity classification. In ECCV, 2010.
    • (2010) ECCV
    • Niebles, J.C.1    Chen, C.-W.2    Fei-Fei, L.3
  • 25
    • 0035328421 scopus 로고    scopus 로고
    • Modeling the shape of the scene: A holistic representation of the spatial envelope
    • A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 42(3):145-175, 2001.
    • (2001) IJCV , vol.42 , Issue.3 , pp. 145-175
    • Oliva, A.1    Torralba, A.2
  • 26
    • 84898791167 scopus 로고    scopus 로고
    • Action and event recognition with Fisher vectors on a compact feature set
    • D. Oneata, J. Verbeek, and C. Schmid. Action and event recognition with Fisher vectors on a compact feature set. In ICCV, 2013.
    • (2013) ICCV
    • Oneata, D.1    Verbeek, J.2    Schmid, C.3
  • 27
    • 84887328711 scopus 로고    scopus 로고
    • Exploring weak stabilization for motion feature extraction
    • D. Park, C. L. Zitnick, D. Ramanan, and P. Doll?ar. Exploring weak stabilization for motion feature extraction. In CVPR, 2013.
    • (2013) CVPR
    • Park, D.1    Zitnick, C.L.2    Ramanan, D.3    Dollar, P.4
  • 29
    • 79959771606 scopus 로고    scopus 로고
    • Improving the Fisher kernel for large-scale image classification
    • F. Perronnin, J. S?anchez, and T. Mensink. Improving the Fisher kernel for large-scale image classification. In ECCV, 2010.
    • (2010) ECCV
    • Perronnin, F.1    Sanchez, J.2    Mensink, T.3
  • 30
    • 84856142160 scopus 로고    scopus 로고
    • Weakly supervised learning of interactions between humans and objects
    • A. Prest, C. Schmid, and V. Ferrari. Weakly supervised learning of interactions between humans and objects. IEEE PAMI, 34(3):601-614, 2012.
    • (2012) IEEE PAMI , vol.34 , Issue.3 , pp. 601-614
    • Prest, A.1    Schmid, C.2    Ferrari, V.3
  • 31
    • 84879550059 scopus 로고    scopus 로고
    • Recognizing 50 human action categories of web videos
    • K. Reddy and M. Shah. Recognizing 50 human action categories of web videos. Machine Vision and Applications, pages 1-11, 2012.
    • (2012) Machine Vision and Applications , pp. 1-11
    • Reddy, K.1    Shah, M.2
  • 32
    • 84866718894 scopus 로고    scopus 로고
    • Action bank: A high-level representation of activity in video
    • S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. In CVPR, 2012.
    • (2012) CVPR
    • Sadanand, S.1    Corso, J.J.2
  • 33
    • 37849037402 scopus 로고    scopus 로고
    • A 3-dimensional SIFT descriptor and its application to action recognition
    • P. Scovanner, S. Ali, and M. Shah. A 3-dimensional SIFT descriptor and its application to action recognition. In ACM Conference on Multimedia, 2007.
    • (2007) ACM Conference on Multimedia
    • Scovanner, P.1    Ali, S.2    Shah, M.3
  • 34
    • 84887327356 scopus 로고    scopus 로고
    • Sampling strategies for real-time action recognition
    • F. Shi, E. Petriu, and R. Laganiere. Sampling strategies for real-time action recognition. In CVPR, 2013.
    • (2013) CVPR
    • Shi, F.1    Petriu, E.2    Laganiere, R.3
  • 35
    • 0028112849 scopus 로고
    • Good features to track
    • J. Shi and C. Tomasi. Good features to track. In CVPR, 1994.
    • (1994) CVPR
    • Shi, J.1    Tomasi, C.2
  • 38
    • 84898475671 scopus 로고    scopus 로고
    • Feature tracking and motion compensation for action recognition
    • H. Uemura, S. Ishikawa, and K. Mikolajczyk. Feature tracking and motion compensation for action recognition. In BMVC, 2008.
    • (2008) BMVC
    • Uemura, H.1    Ishikawa, S.2    Mikolajczyk, K.3
  • 39
    • 84907592231 scopus 로고    scopus 로고
    • Space-variant descriptor sampling for action recognition based on saliency and eye movements
    • E. Vig, M. Dorr, and D. Cox. Space-variant descriptor sampling for action recognition based on saliency and eye movements. In ECCV, 2012.
    • (2012) ECCV
    • Vig, E.1    Dorr, M.2    Cox, D.3
  • 40
    • 84876945537 scopus 로고    scopus 로고
    • Dense trajectories and motion boundary descriptors for action recognition
    • H. Wang, A. Kl?aser, C. Schmid, and C.-L. Liu. Dense trajectories and motion boundary descriptors for action recognition. IJCV, 103(1):60-79, 2013.
    • (2013) IJCV , vol.103 , Issue.1 , pp. 60-79
    • Wang, H.1    Klaser, A.2    Schmid, C.3    Liu, C.-L.4
  • 41
    • 70450196950 scopus 로고    scopus 로고
    • An efficient dense and scaleinvariant spatio-temporal interest point detector
    • G.Willems, T. Tuytelaars, and L. Gool. An efficient dense and scaleinvariant spatio-temporal interest point detector. In ECCV, 2008.
    • (2008) ECCV
    • Willems, G.1    Tuytelaars, T.2    Gool, L.3
  • 42
    • 84863082785 scopus 로고    scopus 로고
    • Action recognition in videos acquired by a moving camera using motion decomposition of Lagrangian particle trajectories
    • S. Wu, O. Oreifej, and M. Shah. Action recognition in videos acquired by a moving camera using motion decomposition of Lagrangian particle trajectories. In ICCV, 2011.
    • (2011) ICCV
    • Wu, S.1    Oreifej, O.2    Shah, M.3
  • 43
    • 77953205594 scopus 로고    scopus 로고
    • Local trinary patterns for human action recognition
    • L. Yeffet and L.Wolf. Local trinary patterns for human action recognition. In ICCV, 2009.
    • (2009) ICCV
    • Yeffet, L.1    Wolf, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.