Open Access
| Issue |
ITM Web Conf.
Volume 7, 2016
3rd Annual International Conference on Information Technology and Applications (ITA 2016)
|
|
|---|---|---|
| Article Number | 05009 | |
| Number of page(s) | 4 | |
| Section | Session 5: Algorithms and Simulation | |
| DOI | https://doi.org/10.1051/itmconf/20160705009 | |
| Published online | 21 November 2016 | |
- Rittscher Jens, Tu Peter H., Krahnstoever Nils, Simultaneous estimation of segmentation and shape, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 486–493(2005) [Google Scholar]
- Saleh S.A.M, Suandi S.A, Ibrahim H., Recent survey on crowd density estimation and counting for visual surveillance[J]. Engineering Applications of Artificial Intelligence, 41,103–114(2015) [CrossRef] [Google Scholar]
- Wu Xinyu, Liang Guoyuan, Lee K.K, Xu Yangsheng, Crowd density estimation using texture analysis and learning, 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO, 214–219 (2006) [Google Scholar]
- Wang B, Bao H, Yang S, et al. Crowd Density Estimation Based on Texture Feature Extraction[J]. Journal of Multimedia, 8(4): 331–337 (2013) [Google Scholar]
- Wang Zhe, Liu Hong, Qian Yueliang, Xu Tao, Crowd density estimation based on local binary pattern cooccurrence matrix, Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW, 372–377 (2012) [CrossRef] [Google Scholar]
- Yang H, Su H, Zheng S, et al. The large-scale crowd density estimation based on sparse spatiotemporal local binary pattern[C]//2011 IEEE International Conference on Multimedia and Expo. IEEE, 1–6 (2011) [Google Scholar]
- Rao Aravinda S, Gubbi Jayavardhana, Marusic Slaven, Stanley Paul, Palaniswami Marimuthu, Crowd density estimation based on optical flow and hierarchical clustering, Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI, 494–499 (2013) [Google Scholar]
- Fu Min, Xu Pei, Li Xudong, Liu Qihe, Ye Mao, Zhu Ce, Fast crowd density estimation with convolutional neural networks, Engineering Applications of Artificial Intelligence, 43, 81–88, August 1 (2015) [CrossRef] [Google Scholar]
- Ojala T., Pietikainen M., Harwood D., Performance evaluation of texture measures with classification based on Kullback discrimination of distributions[C] // Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on. IEEE, 1: 582–585 (1994) [Google Scholar]
- Ojala Timo, Pietikäinen Matti, Mäenpää Topi, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, n 7, 971–987, July (2002) [Google Scholar]
- Mrazová Iveta, Kukacka Marek, Hybrid convolutional neural networks, IEEE International Conference on Industrial Informatics (INDIN), 469–474 (2008) [Google Scholar]
- Y LeCun, K Kavukcuoglu, C. Farabet Convolutional networks and applications in vision[C]//ISCAS, 253–256 (2010) [Google Scholar]
- Sermanet Pierre, Chintala Soumith, Lecun Yann, Convolutional Neural Networks Applied to House Numbers Digit Classification, Proceedings - International Conference on Pattern Recognition, 3288–3291 (2012) [Google Scholar]
- Scherer D, Müller A, Behnke S. Evaluation of pooling operations in convolutional architectures for object recognition[C] // International Conference on Artificial Neural Networks, 92–101 (2010) [Google Scholar]
- http://ftp.pets.rdg.ac.uk/pub/PETS2009/Crowd_PETS09_dataset/a_data/a.html [Google Scholar]
- Saleh Sami Abdulla Mohsen, Suandi Shahrel Azmin, Ibrahim Haidi, Recent survey on crowd density estimation and counting for visual surveillance, Engineering Applications of Artificial Intelligence, 41, 103–114, May 1 (2015) [CrossRef] [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.