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Joint Pedestrian and Body Part Detection via Semantic Relationship Learning
Gu, Junhua1,2; Lan, Chuanxin1,3; Chen, Wenbai4; Han, Hu3
2019-02-02
发表期刊APPLIED SCIENCES-BASEL
ISSN2076-3417
卷号9期号:4页码:14
摘要While remarkable progress has been made to pedestrian detection in recent years, robust pedestrian detection in the wild e.g., under surveillance scenarios with occlusions, remains a challenging problem. In this paper, we present a novel approach for joint pedestrian and body part detection via semantic relationship learning under unconstrained scenarios. Specifically, we propose a Body Part Indexed Feature (BPIF) representation to encode the semantic relationship between individual body parts (i.e., head, head-shoulder, upper body, and whole body) and highlight per body part features, providing robustness against partial occlusions to the whole body. We also propose an Adaptive Joint Non-Maximum Suppression (AJ-NMS) to replace the original NMS algorithm widely used in object detection, leading to higher precision and recall for detecting overlapped pedestrians. Experimental results on the public-domain CUHK-SYSU Person Search Dataset show that the proposed approach outperforms the state-of-the-art methods for joint pedestrian and body part detection in the wild.
关键词joint pedestrian and body part detection adaptive joint non-maximum suppression semantic relationship learning
DOI10.3390/app9040752
收录类别SCI
语种英语
资助项目NSF of Hebei Province through the Key Program[F2016202144]
WOS研究方向Chemistry ; Materials Science ; Physics
WOS类目Chemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号WOS:000460696500138
出版者MDPI
引用统计
被引频次:9[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/4116
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Han, Hu
作者单位1.Hebei Univ Technol, Sch Artificial Intelligence, Tianjin 300401, Peoples R China
2.Hebei Prov Key Lab Big Data Comp, Tianjin 300401, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
4.Beijing Informat Sci & Technol Univ, Sch Automat, Beijing 100101, Peoples R China
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GB/T 7714
Gu, Junhua,Lan, Chuanxin,Chen, Wenbai,et al. Joint Pedestrian and Body Part Detection via Semantic Relationship Learning[J]. APPLIED SCIENCES-BASEL,2019,9(4):14.
APA Gu, Junhua,Lan, Chuanxin,Chen, Wenbai,&Han, Hu.(2019).Joint Pedestrian and Body Part Detection via Semantic Relationship Learning.APPLIED SCIENCES-BASEL,9(4),14.
MLA Gu, Junhua,et al."Joint Pedestrian and Body Part Detection via Semantic Relationship Learning".APPLIED SCIENCES-BASEL 9.4(2019):14.
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