Ambient Sound Helps: Audiovisual Crowd Counting In Extreme ...

Computer Science > Computer Vision and Pattern Recognition arXiv:2005.07097 (cs) [Submitted on 14 May 2020 (v1), last revised 16 May 2020 (this version, v2)] Title:Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions Authors:Di Hu, Lichao Mou, Qingzhong Wang, Junyu Gao, Yuansheng Hua, Dejing Dou, Xiao Xiang Zhu View a PDF of the paper titled Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions, by Di Hu and 6 other authors View PDF

Abstract:Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail to capture informative features in extreme conditions, e.g., imaging at night and occlusion. In this work, we introduce a novel task of audiovisual crowd counting, in which visual and auditory information are integrated for counting purposes. We collect a large-scale benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of 1,935 images and the corresponding audio clips, and 170,270 annotated instances. In order to fuse the two modalities, we make use of a linear feature-wise fusion module that carries out an affine transformation on visual and auditory features. Finally, we conduct extensive experiments using the proposed dataset and approach. Experimental results show that introducing auditory information can benefit crowd counting under different illumination, noise, and occlusion conditions. The dataset and code will be released. Code and data have been made available

Subjects:	Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:2005.07097 [cs.CV]
(or arXiv:2005.07097v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2005.07097 Focus to learn more arXiv-issued DOI via DataCite

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From: Yuansheng Hua [view email] [v1] Thu, 14 May 2020 16:05:47 UTC (7,394 KB) [v2] Sat, 16 May 2020 20:56:26 UTC (7,395 KB) Full-text links:

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