| Pedestrian detection in unseen scenes by dynamically updating visual words | |
Cao, Xianbin1; Wang, Li1; Ning, Bo2; Yuan, Yuan3 ; Yan, Pingkun3
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| 2013-11-07 | |
| 发表期刊 | NEUROCOMPUTING
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| 卷号 | 119页码:232-242 |
| 摘要 | Adapting trained detectors to unseen scenes is a critical problem in pedestrian detection. The performance of trained detector may drop quickly when scenes vary significantly. Retraining a detector with labeled samples from the new scenes may improve its performance. However, it is difficult to obtain enough labeled samples in real applications. In this paper, a novel bag of visual words based method is proposed to detect pedestrians in unseen scenes by dynamically updating the key words. The proposed method achieves its adaptability by using three strategies covering key word selection, detector invariance, and codebook update: (1) In order to select typical words representing pedestrians, a low dimensional model of visual words is built to describe their distribution and select key words using manifold learning. (2) Matching confidence vector (MCV), a novel visual words measurement is proposed, which aims to generate a uniform input vector for the fixed detector applied to different pedestrian codebooks. (3) When detecting pedestrians under changing road conditions, the key word set will be dynamically adjusted according to the matching frequency of each word to adapt the detector to the new scenes. By employing the above strategies, the proposed method is able to detect pedestrians in different scenes without retraining the detector. Experiments in different scenes showed that our proposed method can achieve better adaptability to various scenes and get better performance than other existing methods in unseen scenes. (C) 2013 Elsevier B.V. All rights reserved. |
| 文章类型 | Article |
| 关键词 | Pedestrian Detection Adaptive Detector Bag Of Visual Words Manifold Leaning |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.neucom.2013.03.036 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | NONLINEAR DIMENSIONALITY REDUCTION ; OBJECT DETECTION ; FEATURES |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:000323851800029 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/23470 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Beihang Univ, Beijing 100191, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Xianbin,Wang, Li,Ning, Bo,et al. Pedestrian detection in unseen scenes by dynamically updating visual words[J]. NEUROCOMPUTING,2013,119:232-242. |
| APA | Cao, Xianbin,Wang, Li,Ning, Bo,Yuan, Yuan,&Yan, Pingkun.(2013).Pedestrian detection in unseen scenes by dynamically updating visual words.NEUROCOMPUTING,119,232-242. |
| MLA | Cao, Xianbin,et al."Pedestrian detection in unseen scenes by dynamically updating visual words".NEUROCOMPUTING 119(2013):232-242. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Pedestrian detection（10585KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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