| Speed up deep neural network based pedestrian detection by sharing features across multi-scale models | |
Jiang, Xiaoheng1; Pang, Yanwei1; Li, Xuelong2 ; Pan, Jing1,3
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| 作者部门 | 光学影像学习与分析中心 |
| 2016-04-12 | |
| 发表期刊 | NEUROCOMPUTING
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| ISSN | 0925-2312 |
| 卷号 | 185页码:163-170 |
| 产权排序 | 2 |
| 摘要 | Deep neural networks (DNNs) have now demonstrated state-of-the-art detection performance on pedestrian datasets. However, because of their high computational complexity, detection efficiency is still a frustrating problem even with the help of Graphics Processing Units (GPUs). To improve detection efficiency, this paper proposes to share features across a group of DNNs that correspond to pedestrian models of different sizes. By sharing features, the computational burden for extracting features from an image pyramid can be significantly reduced. Simultaneously, we can detect pedestrians of several different scales on one single layer of an image pyramid. Furthermore, the improvement of detection efficiency is achieved with negligible loss of detection accuracy. Experimental results demonstrate the robustness and efficiency of the proposed algorithm. (C) 2015 The Authors. Published by Elsevier B.V. |
| 文章类型 | Article |
| 关键词 | Pedestrian Detection Deep Neural Networks Convolutional Neural Networks Share Features |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.neucom.2015.12.042 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | OBJECT DETECTION |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| 项目资助者 | National Basic Research Program of China (973 Program)(2014CB340400) ; National Natural Science Foundation of China(61172121 ; Chinese Academy of Sciences(KGZD-EW-T03) ; Tianjin University of Technology and Education(RC14-46) ; 61271412 ; 61472274 ; 61222109 ; 61503274) |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:000374363900017 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/28091 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China 2.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 3.Tianjin Univ Technol & Educ, Sch Elect Engn, Tianjin 300222, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Xiaoheng,Pang, Yanwei,Li, Xuelong,et al. Speed up deep neural network based pedestrian detection by sharing features across multi-scale models[J]. NEUROCOMPUTING,2016,185:163-170. |
| APA | Jiang, Xiaoheng,Pang, Yanwei,Li, Xuelong,&Pan, Jing.(2016).Speed up deep neural network based pedestrian detection by sharing features across multi-scale models.NEUROCOMPUTING,185,163-170. |
| MLA | Jiang, Xiaoheng,et al."Speed up deep neural network based pedestrian detection by sharing features across multi-scale models".NEUROCOMPUTING 185(2016):163-170. |
| 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 | ||
| Speed up deep neural（867KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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