Speed up deep neural network based pedestrian detection by sharing features across multi-scale models
文献类型：期刊论文
| 作者 | Jiang, Xiaoheng1; Pang, Yanwei1; Li, Xuelong2 ; Pan, Jing1,3
|
| 刊名 | neurocomputing
 |
| 出版日期 | 2016-04-12 |
| 卷号 | 185页码:163-170 |
| 关键词 | Pedestrian detection Deep neural networks Convolutional neural networks Share features |
| ISSN号 | 0925-2312 |
| 产权排序 | 2 |
| 英文摘要 | 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. |
| 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 |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000374363900017 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/28091]  |
| 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.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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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