Granular-based dense crowd density estimation

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

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Official URL: https://doi.org/10.1007/s11042-017-5418-y

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
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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