Open Access
| Issue |
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|
|
|---|---|---|
| Article Number | 01025 | |
| Number of page(s) | 5 | |
| Section | Session 1: Robotics | |
| DOI | https://doi.org/10.1051/itmconf/20171201025 | |
| Published online | 05 September 2017 | |
- Wang H, Klaser A, Schmid C, Liu C L. Action recognition by dense trajectories. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, RI: IEEE, 2011. 3169–3176 [CrossRef] [Google Scholar]
- Soomro K, Zamir A. Action recognition in realistic sports videos. Computer Vision in Sports. 2014. [Google Scholar]
- Niu F, Adbel-Mottaleb M, HMM-based Segmentation and Recognition of Human Activities from Video Sequences[C], IEEE International Conference on Multimedia and Expo, Amsterdam, Netherlands, 2005 [Google Scholar]
- Zhou Hanning, Kimber D. Unusual Event Detection Via Multi-Camera Video Mining[C]. 18th International Conference on Pattern Recognition, Hong Kong, China, 2006 [Google Scholar]
- Brand M, Oliver N and Pentland A. Coupled hidden Markov models for complex action recognition. In: Proc IEEE Conference Computer Vision and Pattern Recognition, Puerto Rico, 1997, 994–999. [CrossRef] [Google Scholar]
- Chen Yufeng, Liang Guoyuan Lee K. Abnormal Behavior Detection by Multi-SVM based Beyesian Betwork[C]. International Conference on Information Acquistion. Guangzhou, China, 2010. [Google Scholar]
- Le Cun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278–2324 [CrossRef] [Google Scholar]
- Wang H, Schmid C. Dense trajectories and motion boundary descriptors for action recognition[J]. International Journal of Computer Vision, 2013, 103(1): 60–79 [CrossRef] [MathSciNet] [Google Scholar]
- Zhang Fan, Gao li, Lu Haixian. Star Skeleton for Human Behavior Recognition[C]. IEEE International Conference on Audio, Language and Image Processing, Shanghai, China, 2012. [Google Scholar]
- H. Wang and C. Schmid,Action recognition with improved trajectories, in ICCV, 2013. [Google Scholar]
- H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, HMDB:a large video database for human motion recognition, in ICCV, 2011. [Google Scholar]
- I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350. [Google Scholar]
- M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt, Action classification in soccer videos with long short-term memory. recurrent neural networks, in International Conference on Artificial Neural Networks (ICANN), 2010. [Google Scholar]
- A. Farhadi, M. Hejrati, M. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth, Every picture tells a story: Generating sentences from images, in ECCV, 2010. [Google Scholar]
- G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. C. Berg, and T. L. Berg, Baby talk: Understanding and generating simple image descriptions, in CVPR, 2011. [Google Scholar]
- Y. Yang, C. L. Teo, H. Daum´e III, and Y. Aloimonos, Corpusguided sentence generation of natural images, in EMNLP, 2011. [Google Scholar]
- M. Mitchell, X. Han, J. Dodge, A. Mensch, A. Goyal, A. Berg, K. Yamaguchi, T. Berg, K. Stratos, and H. Daum´e III, Midge: Generating image descriptions from computer vision detections, 2012 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.