메뉴 건너뛰기




Volumn 2015-February, Issue , 2016, Pages 303-311

Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action Recognition

Author keywords

Computational modeling; Data mining; Dictionaries; Feature extraction; Histograms; Skeleton; Three dimensional displays

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; DATA MINING; LEARNING ALGORITHMS; MUSCULOSKELETAL SYSTEM; RECURRENT NEURAL NETWORKS;

EID: 84962018283     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2015.48     Document Type: Conference Paper
Times cited : (16)

References (31)
  • 2
    • 85112851150 scopus 로고    scopus 로고
    • Poselets: Body part detectors trained using 3D human pose annotations
    • 3
    • L. Bourdev and J. Malik. Poselets: Body part detectors trained using 3D human pose annotations. In IEEE International Conference on Computer Vision, pages 1365-1372, 2009. 3
    • (2009) IEEE International Conference on Computer Vision , pp. 1365-1372
    • Bourdev, L.1    Malik, J.2
  • 12
    • 0032203257 scopus 로고    scopus 로고
    • Gradientbased learning applied to document recognition
    • 2, 3, 5
    • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradientbased learning applied to document recognition. In Proceedings of the IEEE, pages 2278-2324, 1998. 2, 3, 5
    • (1998) Proceedings of the IEEE , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 15
    • 84898788017 scopus 로고    scopus 로고
    • Hierarchical joint maxmargin learning of mid and top level representations for visual recognition
    • 3
    • H.-A. Lobel, A. Soto, and R. Vidal. Hierarchical joint maxmargin learning of mid and top level representations for visual recognition. In IEEE International Conference on Computer Vision, 2013. 3
    • (2013) IEEE International Conference on Computer Vision
    • Lobel, H.-A.1    Soto, A.2    Vidal, R.3
  • 24
    • 84876945537 scopus 로고    scopus 로고
    • Dense trajectories and motion boundary descriptors for action recognition
    • 2
    • H. Wang, A. Klaser, C. Schmid, and C. Liu. Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision, 103(1):60-79, 2013. 2
    • (2013) International Journal of Computer Vision , vol.103 , Issue.1 , pp. 60-79
    • Wang, H.1    Klaser, A.2    Schmid, C.3    Liu, C.4
  • 29
    • 84898773205 scopus 로고    scopus 로고
    • The moving pose: An efficient 3D kinematics descriptor for low-latency action recognition and detection
    • 2, 5, 6
    • M. Zanfir, M. Leordeanu, and C. Sminchisescu. The moving pose: An efficient 3D kinematics descriptor for low-latency action recognition and detection. In IEEE International Conference on Computer Vision, 2013. 2, 5, 6
    • (2013) IEEE International Conference on Computer Vision
    • Zanfir, M.1    Leordeanu, M.2    Sminchisescu, C.3
  • 30
    • 84898795297 scopus 로고    scopus 로고
    • From actemes to action: A strongly-supervised representation for detailed action understanding
    • 2
    • W. Zhang, M. Zhu, and K. Derpanis. From actemes to action: A strongly-supervised representation for detailed action understanding. In IEEE International Conference on Computer Vision, 2013. 2
    • (2013) IEEE International Conference on Computer Vision
    • Zhang, W.1    Zhu, M.2    Derpanis, K.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.