Scale and Background Aware Asymmetric Bilateral Network for Unconstrained Image Crowd Counting
文献类型：期刊论文
| 作者 | Lv, Gang2,3,4; Xu, Yushan2; Ma, Zuchang3,4 ; Sun, Yining3,4; Nian, Fudong1,2
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| 刊名 | MATHEMATICS
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| 出版日期 | 2022-04-01 |
| 卷号 | 10 |
| 关键词 | crowd counting asymmetric structure bilateral network density estimation |
| DOI | 10.3390/math10071053 |
| 通讯作者 | Nian, Fudong(nianfd@hfuu.edu.cn) |
| 英文摘要 | This paper attacks the two challenging problems of image-based crowd counting, that is, scale variation and complex background. To that end, we present a novel crowd counting method, called the Scale and Background aware Asymmetric Bilateral Network (SBAB-Net), which is able to handle scale variation and background noise in a unified framework. Specifically, the proposed SBAB-Net contains three main components, a pre-trained backbone convolutional neural network (CNN) as the feature extractor and two asymmetric branches to generate a density map. These two asymmetric branches have different structures and use features from different semantic layers. One branch is densely connected stacked dilated convolution (DCSDC) sub-network with different dilation rates, which relies on one deep feature layer and can handle scale variation. The other branch is parameter-free densely connected stacked pooling (DCSP) sub-network with various pooling kernels and strides, which relies on shallow feature and can fuse features with several receptive fields to reduce the impact of background noise. Two sub-networks are fused by the attention mechanism to generate the final density map. Extensive experimental results on three widely-used benchmark datasets have demonstrated the effectiveness and superiority of our proposed method: (1) We achieve competitive counting performance compared to state-of-the-art methods; (2) Compared with baseline, the MAE and MSE are decreased by at least 6.3% and 11.3%, respectively. |
| WOS关键词 | PEOPLE |
| 资助项目 | University Synergy Innovation Program of Anhui Province[GXXT-2019-048] ; National Natural Science Foundation (NSF) of China[61902104] ; National Key RD Program[2020YFC2005603] ; Anhui Provincial Natural Science Foundation[2008085QF295] ; University Natural Sciences Research Project of Anhui Province[KJ2020A0651] |
| WOS研究方向 | Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000781486200001 |
| 出版者 | MDPI |
| 资助机构 | University Synergy Innovation Program of Anhui Province ; National Natural Science Foundation (NSF) of China ; National Key RD Program ; Anhui Provincial Natural Science Foundation ; University Natural Sciences Research Project of Anhui Province |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/128725]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Nian, Fudong |
| 作者单位 | 1.Anhui Univ, Sch Artificial Intelligence, Hefei 230601, Peoples R China 2.Hefei Univ, Sch Adv Mfg Engn, Hefei 230601, Peoples R China 3.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Peoples R China 4.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lv, Gang,Xu, Yushan,Ma, Zuchang,et al. Scale and Background Aware Asymmetric Bilateral Network for Unconstrained Image Crowd Counting[J]. MATHEMATICS,2022,10. |
| APA | Lv, Gang,Xu, Yushan,Ma, Zuchang,Sun, Yining,&Nian, Fudong.(2022).Scale and Background Aware Asymmetric Bilateral Network for Unconstrained Image Crowd Counting.MATHEMATICS,10. |
| MLA | Lv, Gang,et al."Scale and Background Aware Asymmetric Bilateral Network for Unconstrained Image Crowd Counting".MATHEMATICS 10(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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