Cross-Level Parallel Network for Crowd Counting
文献类型：期刊论文
| 作者 | Li, Jing3 ; Xue, Yaokai3; Wang, Weiqun4; Ouyang, Gaoxiang1,2
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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| 出版日期 | 2020 |
| 卷号 | 16期号:1页码:566-576 |
| 关键词 | Convolutional neural network (CNN) cross-level and multiscale features crowd counting density map scale aggregation network |
| ISSN号 | 1551-3203 |
| DOI | 10.1109/TII.2019.2935244 |
| 通讯作者 | Ouyang, Gaoxiang(ouyang@bnu.edu.cn) |
| 英文摘要 | Automated people counting in crowd scenes is challenging due to large variations in scale, density, and background clutter. To tackle them, we propose a novel cross-level parallel network (CLPNet) by extracting multiple low-level features from VGG16 and fusing them with specific scale aggregation modules in the high-level stage. To deal with scale variation, we design five different aggregation modules for multiscale fusion. Furthermore, the ground truth is processed skillfully to eliminate the mismatches caused by the scale variation between heads and density maps. To cope with background clutter, cross-level feature fusion is implemented. Higher-level semantic information could effectively separate head from background and regain the lost low-level detailed information. To address the variation of density, we design a parallel network, in which two separate channels focus on different density-level estimation, and attain more accurate counting results. Finally, we evaluate the proposed CLPNet on four representative crowd counting datasets, i.e., ShanghaiTech, UCF_CC_50, WorldExpo'10, and UCF_QNRF. The experimental results demonstrate that with the cross-level and multiscale structure CLPNet achieves superior performance compared with the state-of-the-art crowd counting methods. |
| WOS关键词 | SCALE ; IMAGE |
| 资助项目 | National Key Research and Development Program of China[2017YFC0820205] ; National Natural Science Foundation of China[61703198] ; Natural Science Foundation for Distinguished Young Scholars of Jiangxi Province[2018ACB21014] ; Open Fund of State Key Laboratory of Management and Control for Complex Systems[20180109] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000508428900054 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Natural Science Foundation for Distinguished Young Scholars of Jiangxi Province ; Open Fund of State Key Laboratory of Management and Control for Complex Systems |
| 源URL | [http://ir.ia.ac.cn/handle/173211/29543]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Ouyang, Gaoxiang |
| 作者单位 | 1.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China 2.Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China 3.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Jing,Xue, Yaokai,Wang, Weiqun,et al. Cross-Level Parallel Network for Crowd Counting[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2020,16(1):566-576. |
| APA | Li, Jing,Xue, Yaokai,Wang, Weiqun,&Ouyang, Gaoxiang.(2020).Cross-Level Parallel Network for Crowd Counting.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,16(1),566-576. |
| MLA | Li, Jing,et al."Cross-Level Parallel Network for Crowd Counting".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 16.1(2020):566-576. |
入库方式： OAI收割
来源：自动化研究所
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