Kok, Ven Jyn and Chan, Chee Seng (2018) Granular-based dense crowd density estimation. Multimedia Tools and Applications, 77 (15). pp. 20227-20246. ISSN 1380-7501, DOI https://doi.org/10.1007/s11042-017-5418-y.
Full text not available from this repository.Abstract
Dense crowd density estimation is one of the fundamental tasks in crowd analysis. While tremendous progress has been made to understand crowd scenes along with the rise of Convolutional Neural Networks (CNNs), research work on dense crowd density estimation is still an ongoing process. In this paper, we propose a novel approach to learn discriminative crowd features from granules, that conforms to the outline between crowd and background (i.e. non-crowd) regions, for density estimation. It shows that by studying the inner statistics of granules for density estimation, this approach is adaptive to arbitrary distribution of crowd (i.e. scene independent). Multiple features fusion is proposed to learn discriminative crowd features from granules. This is to be used as description of the crowd where a direct mapping between the features and crowd density is learned. Extensive experiments on public benchmark datasets demonstrate the effectiveness of our novel approach for scene independent dense crowd density estimation.
| Item Type: | Article |
|---|---|
| Funders: | GGPM grant GGPM-2017-024, from the National University of Malaysia (UKM), Fundamental Research Grant Scheme (FRGS) MoHE Grant FP070-2015A, from the Ministry of Education Malaysia |
| Uncontrolled Keywords: | Dense crowd analysis; Density estimation; Texture features; Visual surveillance |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 09 Jul 2019 08:09 |
| Last Modified: | 09 Jul 2019 08:09 |
| URI: | http://eprints.um.edu.my/id/eprint/21595 |
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