Transfer learning for pedestrian detection
文献类型：期刊论文
| 作者 | Cao, Xianbin2; Wang, Zhong3; Yan, Pingkun1; Li, Xuelong1
|
| 刊名 | neurocomputing
 |
| 出版日期 | 2013-01-16 |
| 卷号 | 100页码:51-57 |
| 关键词 | Pedestrian detection Scene change Manifold learning Transfer learning |
| 英文摘要 | most of the existing methods for pedestrian detection work well, only when the following assumption is satisfied: the features extracted from the training dataset and the testing dataset have very similar distributions in the feature space. however, in practice, this assumption does not hold because of the scene complexity and variation. in this paper, a new method is proposed for detecting pedestrians in various scenes based on the transfer learning technique. our proposed method employs the following two strategies for improving the pedestrian detection performance. first, a new sample screening method based on manifold learning is proposed. the basic idea is to choose samples from the training set, which may be similar to the samples from the unseen scene, and then merge the selected samples into the unseen set. second, a new classification model based on transfer learning is proposed. the advantage of the classification model is that only a small number of samples need to be used from the unseen scenes. most of the training samples are still obtained from the training scene, which take up to 90% of the entire training samples. compared to the traditional pedestrian detection methods, the proposed algorithm can adapt to different scenes for detecting pedestrians. experiments on two pedestrian detection benchmark datasets, dc and nicta, showed that the method can obtain better performance as compared to other previous methods. (c) 2012 elsevier b.v. all rights reserved. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | object detection ; tracking ; system |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000312175500006 |
| 公开日期 | 2015-06-30 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/23439]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 2.BeiHang Univ, Beijing 100083, Peoples R China 3.Univ Sci & Technol China, Hefei 230026, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Xianbin,Wang, Zhong,Yan, Pingkun,et al. Transfer learning for pedestrian detection[J]. neurocomputing,2013,100:51-57. |
| APA | Cao, Xianbin,Wang, Zhong,Yan, Pingkun,&Li, Xuelong.(2013).Transfer learning for pedestrian detection.neurocomputing,100,51-57. |
| MLA | Cao, Xianbin,et al."Transfer learning for pedestrian detection".neurocomputing 100(2013):51-57. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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