A parallel vision approach to scene-specific pedestrian detection
文献类型：期刊论文
| 作者 | Zhang, Wenwen1,2; Wang, Kunfeng2 ; Liu, Yating2,3; Lu, Yue2,3; Wang, Fei-Yue2
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| 刊名 | NEUROCOMPUTING
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| 出版日期 | 2020-06-21 |
| 卷号 | 394页码:114-126 |
| 关键词 | Pedestrian detection Specific scene Synthetic data Video surveillance Parallel vision |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.03.095 |
| 通讯作者 | Wang, Kunfeng(kunfeng.wang@ia.ac.cn) |
| 英文摘要 | In recent years, with the development of computing power and deep learning algorithms, pedestrian detection has made great progress. Nevertheless, once a detection model trained on generic datasets (such as PASCAL VOC and MS COCO) is applied to a specific scene, its precision is limited by the distribution gap between the generic data and the specific scene data. It is difficult to train the model for a specific scene, due to the lack of labeled data from that scene. Even though we manage to get some labeled data from a specific scene, the changing environmental conditions make the pre-trained model perform bad. In light of these issues, we propose a parallel vision approach to scene-specific pedestrian detection. Given an object detection model, it is trained via two sequential stages: (1) the model is pre-trained on augmented-reality data, to address the lack of scene-specific training data; (2) the pre-trained model is incrementally optimized with newly synthesized data as the specific scene evolves over time. On publicly available datasets, our approach leads to higher precision than the models trained on generic data. To tackle the dynamically changing scene, we further evaluate our approach on the webcam data collected from Church Street Market Place, and the results are also encouraging. (C) 2019 Elsevier B.V. All rights reserved. |
| WOS关键词 | CAMERA NETWORKS |
| 资助项目 | National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[U1811463] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000531730600012 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/39436]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Wang, Kunfeng |
| 作者单位 | 1.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Wenwen,Wang, Kunfeng,Liu, Yating,et al. A parallel vision approach to scene-specific pedestrian detection[J]. NEUROCOMPUTING,2020,394:114-126. |
| APA | Zhang, Wenwen,Wang, Kunfeng,Liu, Yating,Lu, Yue,&Wang, Fei-Yue.(2020).A parallel vision approach to scene-specific pedestrian detection.NEUROCOMPUTING,394,114-126. |
| MLA | Zhang, Wenwen,et al."A parallel vision approach to scene-specific pedestrian detection".NEUROCOMPUTING 394(2020):114-126. |
入库方式： OAI收割
来源：自动化研究所
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