Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Reference :

    1. Aggarwal, J. K., and Cai, Q. (1999). Human motion analysis: a review. Comput. Vis. Image Understand. 73, 428–440. doi:10.1006/cviu.1998.0744 CrossRef Full Text | Google Scholar
    2. Aggarwal, J. K., and Ryoo, M. S. (2011). Human activity analysis: a review. ACM Comput.Surv. 43, 1–43. doi:10.1145/1922649.1922653 CrossRef Full Text | Google Scholar
    3. Aggarwal, J. K., and Xia, L. (2014). Human activity recognition from 3D data: a review. Pattern Recognit.Lett. 48, 70–80. doi:10.1016/j.patrec.2014.04.011 CrossRef Full Text | Google Scholar
    4. Akata, Z., Perronnin, F., Harchaoui, Z., and Schmid, C. (2013).“Label-embedding for attribute-based classification,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Portland, OR), 819–826. Google Scholar
    5. Alahi, A., Ramanathan, V., and Fei-Fei, L. (2014).“Socially-aware large-scale crowd forecasting,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Columbus, OH), 2211– 2218. Google Scholar
    6. AlZoubi, O., Fossati, D., D’Mello, S. K., and Calvo, R. A. (2013).“Affect detection and classification from the non-stationary physiological data,” in Proc. International Conference on Machine Learning and Applications (Portland, OR), 240–245. Google Scholar
    7. Amer, M. R., and Todorovic, S. (2012). “Sum-product networks for modeling activities with stochastic structure,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Providence, RI), 1314–1321. Google Scholar
    8. Amin, S., Andriluka, M., Rohrbach, M., and Schiele, B. (2013).“Multi-view pictorial structures for 3D human pose estimation,” in Proc. British Machine Vision Conference (Bristol), 1–12. Google Scholar
    9. Andriluka, M., Pishchulin, L., Gehler, P. V., and Schiele, B. (2014).“2D human pose estimation: new benchmark and state of the art analysis,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Columbus, OH), 3686–3693. Google Scholar
    10. Andriluka, M., and Sigal, L. (2012). “Human context: modeling human-human interactions for monocular 3D pose estimation,” in Proc. International Conference on Articulated Motion and Deformable Objects (Mallorca: Springer-Verlag), 260–272. Google Scholar
    11. Anirudh, R., Turaga, P., Su, J., and Srivastava, A. (2015).“Elastic functional coding of human actions: from vector-fields to latent variables,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Boston, MA), 3147–3155. Google Scholar
    12. Atrey, P. K., Hossain, M. A., El-Saddik, A., and Kankanhalli, M. S. (2010). Multimodal fusion for multimedia analysis: a survey. Multimed. Syst. 16, 345– 379. doi:10.1007/s00530-010-0182-0

Recent Article