Granular-based dense crowd density estimation
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...
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| Format: | Article |
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Springer
2018
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| _version_ | 1825721714592448512 |
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| author | Kok, Ven Jyn Chan, Chee Seng |
| author_facet | Kok, Ven Jyn Chan, Chee Seng |
| author_sort | Kok, Ven Jyn |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:54:34Z |
| format | Article |
| id | um.eprints-21595 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:54:34Z |
| publishDate | 2018 |
| publisher | Springer |
| record_format | dspace |
| spelling | um.eprints-215952019-07-09T08:09:13Z http://eprints.um.edu.my/21595/ Granular-based dense crowd density estimation Kok, Ven Jyn Chan, Chee Seng QA75 Electronic computers. Computer science 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. Springer 2018 Article PeerReviewed 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 <https://doi.org/10.1007/s11042-017-5418-y>. https://doi.org/10.1007/s11042-017-5418-y doi:10.1007/s11042-017-5418-y |
| spellingShingle | QA75 Electronic computers. Computer science Kok, Ven Jyn Chan, Chee Seng Granular-based dense crowd density estimation |
| title | Granular-based dense crowd density estimation |
| title_full | Granular-based dense crowd density estimation |
| title_fullStr | Granular-based dense crowd density estimation |
| title_full_unstemmed | Granular-based dense crowd density estimation |
| title_short | Granular-based dense crowd density estimation |
| title_sort | granular based dense crowd density estimation |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT kokvenjyn granularbaseddensecrowddensityestimation AT chancheeseng granularbaseddensecrowddensityestimation |