Density-Aware Multi-Task Learning for Crowd Counting
文献类型：期刊论文
| 作者 | Jiang, Xiaoheng2; Zhang, Li2; Zhang, Tianzhu3 ; Lv, Pei2; Zhou, Bing2; Pang, Yanwei4; Xu, Mingliang2; Xu, Changsheng1
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2021 |
| 卷号 | 23页码:443-453 |
| 关键词 | Task analysis Semantics Estimation Feature extraction Convolutional neural networks Cameras Head Convolutional neural network crowd counting density-level classification density map estimation multi-task learning |
| ISSN号 | 1520-9210 |
| DOI | 10.1109/TMM.2020.2980945 |
| 通讯作者 | Xu, Mingliang(iexumingliang@zzu.edu.cn) |
| 英文摘要 | In this paper, we present a method called density-aware convolutional neural network (DensityCNN) to perform the crowd counting task in various crowded scenes. The key idea of the DensityCNN is to utilize high-level semantic information to provide guidance and constraint when generating density maps. To this end, we implement the DensityCNN by adopting a multi-task CNN structure to jointly learn density-level classification and density map estimation. The density-level classification task learns multi-channel semantic features that are aware of the density distributions of the input image. This task is accomplished via our specially designed group-based convolutional structure in a supervised learning manner. In the density map estimation task, these semantic features are deployed together with high-dimension convolutional features to generate density maps with lower count errors. Extensive experiments on four challenging crowd datasets (ShanghaiTech, UCF_CC_50, UCF-QNCF, and WorldExpo'10) and one vehicle dataset TRANCOS demonstrate the effectiveness of the proposed method. |
| WOS关键词 | DEEP |
| 资助项目 | National Natural Science Foundation of China[61802351] ; National Natural Science Foundation of China[61822701] ; National Natural Science Foundation of China[61872324] ; National Natural Science Foundation of China[61772474] ; China Postdoctoral Science Foundation[2018M632802] ; Key R&D and Promotion Projects in Henan Province[192102310258] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000601877600035 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Key R&D and Promotion Projects in Henan Province |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42826]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 通讯作者 | Xu, Mingliang |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Peoples R China 3.Univ Sci & Technol China, Hefei 230026, Peoples R China 4.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Xiaoheng,Zhang, Li,Zhang, Tianzhu,et al. Density-Aware Multi-Task Learning for Crowd Counting[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:443-453. |
| APA | Jiang, Xiaoheng.,Zhang, Li.,Zhang, Tianzhu.,Lv, Pei.,Zhou, Bing.,...&Xu, Changsheng.(2021).Density-Aware Multi-Task Learning for Crowd Counting.IEEE TRANSACTIONS ON MULTIMEDIA,23,443-453. |
| MLA | Jiang, Xiaoheng,et al."Density-Aware Multi-Task Learning for Crowd Counting".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):443-453. |
入库方式： OAI收割
来源：自动化研究所
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