Pedestrian Counting With Back-Propagated Information and Target Drift Remedy
文献类型：期刊论文
| 作者 | Chen, Ke1; Zhang, Zhaoxiang2 ; Zhaoxiang Zhang
|
| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
 |
| 出版日期 | 2017-04-01 |
| 卷号 | 47期号:4页码:639-647 |
| 关键词 | Back-propagated Cumulative Attributes Pedestrian Counting Regression Learning Visual Surveillance |
| DOI | 10.1109/TSMC.2016.2618916 |
| 文献子类 | Article |
| 英文摘要 | Pedestrian density is one of the important factors in designing visual surveillance and intelligent transportation systems, but it is challenging to obtain accurate and robust estimates because of both inconsistent crowd patterns in the scenes and target drift caused by imbalanced data distribution. Most of existing global regression frameworks focus on the former challenge to improve the robustness of regression learning, but very few work concerns on mitigating the suffering from the latter one. This paper proposes a novel counting-by-regression framework to utilize the importance of training samples to improve the robustness against inconsistent feature-target relationship based on a recently-proposed learning paradigm-learning with privileged information. To this end, the concept of back-propagation is for the first time considered to select more informative samples contributed to robust fitting performance. Moreover, the direction of target drift along the continuously-changing target dimension is discovered by learning local classifiers under different situation of pedestrian density, which can thus be exploited in our algorithm to further boost the performance. Experimental evaluation on the public UCSD and shopping Mall benchmarks verifies that our approach significantly beats the state-of-the-art counting-by-regression frameworks. |
| WOS关键词 | AGE ESTIMATION ; CROWD DENSITY ; CLASSIFICATION ; REGRESSION ; PEOPLE |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000398966700006 |
| 资助机构 | Academy of Finland(267581 ; National Natural Science Foundation of China(61375036 ; 298700) ; 61511130079) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14034]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhaoxiang Zhang |
| 作者单位 | 1.Tampere Univ Technol, Dept Signal Proc, FIN-33101 Tampere, Finland 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Ke,Zhang, Zhaoxiang,Zhaoxiang Zhang. Pedestrian Counting With Back-Propagated Information and Target Drift Remedy[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2017,47(4):639-647. |
| APA | Chen, Ke,Zhang, Zhaoxiang,&Zhaoxiang Zhang.(2017).Pedestrian Counting With Back-Propagated Information and Target Drift Remedy.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,47(4),639-647. |
| MLA | Chen, Ke,et al."Pedestrian Counting With Back-Propagated Information and Target Drift Remedy".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 47.4(2017):639-647. |
入库方式： OAI收割
来源：自动化研究所
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