Knowledge Commons of Institute of Automation,CAS
A fast fused part-based model with new deep feature for pedestrian detection and security monitoring | |
Cheng, Eric Juwei1; Prasad, Mukesh2; Yang, Jie2; Khanna, Pritee3; Chen, Bing-Hong1; Tao, Xian4; Young, Ku-Young1; Lin, Chin-Teng2 | |
发表期刊 | MEASUREMENT |
ISSN | 0263-2241 |
2020-02-01 | |
卷号 | 151期号:1页码:12 |
摘要 | In recent years, pedestrian detection based on computer vision has been widely used in intelligent transportation, security monitoring, assistance driving and other related applications. However, one of the remaining open challenges is that pedestrians are partially obscured and their posture changes. To address this problem, deformable part model (DPM) uses a mixture of part filters to capture variation in view point and appearance and achieves success for challenging datasets. Nevertheless, the expensive computation cost of DPM limits its ability in the real-time application. This study propose a fast fused part-based model (FFPM) for pedestrian detection to detect the pedestrians efficiently and accurately in the crowded environment. The first step of the proposed method trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. These six response feature maps are combined with full-body model to produce spatial deep features. The second step of the proposed method uses the deep features as an input to support vector machine (SVM) to detect pedestrian. A variety of strategies is introduced in the proposed model, including part-based to full-body method, spatial filtering, and multi-ratios combination. Experiment results show that the proposed FFPM method improves the computation speed of DPM and maintains the performance in detection. (C) 2019 Elsevier Ltd. All rights reserved. |
关键词 | Pedestrian detection Haar-like feature Deep fused feature Deformable partmodel Security monitoring |
DOI | 10.1016/j.measurement.2019.107081 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Australian Re-search Council (ARC)[DP180100670] ; Australian Re-search Council (ARC)[DP180100656] ; Army Research Laboratory ; Taiwan Ministry of Science and Technology MOST[106-2218-E-009-027-MY3] ; [W911NF-10-2-0022] ; [W911NF-10-D-0 0 02/TO 0 023] ; Australian Re-search Council (ARC)[DP180100670] ; Australian Re-search Council (ARC)[DP180100656] ; Army Research Laboratory ; Taiwan Ministry of Science and Technology MOST[106-2218-E-009-027-MY3] ; [W911NF-10-2-0022] ; [W911NF-10-D-0 0 02/TO 0 023] |
WOS研究方向 | Engineering ; Instruments & Instrumentation |
WOS类目 | Engineering, Multidisciplinary ; Instruments & Instrumentation |
WOS记录号 | WOS:000500942200046 |
出版者 | ELSEVIER SCI LTD |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/29374 |
专题 | 中国科学院工业视觉智能装备工程实验室_精密感知与控制 |
通讯作者 | Prasad, Mukesh |
作者单位 | 1.Natl Chaio Tung Univ, Dept Elect Engn, Hsinchu, Taiwan 2.Univ Technol Sydney, FEIT, Sch Comp Sci, Ctr Artificial Intelligence, Sydney, NSW, Australia 3.Indian Inst Informat Technol Design & Mfg Jabalpu, Comp Sci & Engn Discipline, Jabalpur, India 4.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Cheng, Eric Juwei,Prasad, Mukesh,Yang, Jie,et al. A fast fused part-based model with new deep feature for pedestrian detection and security monitoring[J]. MEASUREMENT,2020,151(1):12. |
APA | Cheng, Eric Juwei.,Prasad, Mukesh.,Yang, Jie.,Khanna, Pritee.,Chen, Bing-Hong.,...&Lin, Chin-Teng.(2020).A fast fused part-based model with new deep feature for pedestrian detection and security monitoring.MEASUREMENT,151(1),12. |
MLA | Cheng, Eric Juwei,et al."A fast fused part-based model with new deep feature for pedestrian detection and security monitoring".MEASUREMENT 151.1(2020):12. |
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 | ||
ren.pdf(3849KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论