Urban scene based Semantical Modulation for Pedestrian Detection

doi:10.1016/j.neucom.2021.11.091

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Urban scene based Semantical Modulation for Pedestrian Detection
	Jiang, Hangzhi 1,2; Liao, Shengcai 3; Li, Jinpeng 3; Prinet, Veronique 2; Xiang, Shiming1,2
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2022-02-14
卷号	474 页码:1-12
通讯作者	Jiang, Hangzhi(jianghangzhi2018@ia.ac.cn)
摘要	Despite recent progress, pedestrian detection still suffers from the troublesome problems of small objects, occlusions, and numerous false positives. Intuitively, the rich context information available from urban scenes could help determine the presence and location of pedestrians. For example, roads and sidewalks are good cues for potential pedestrians, while detections on buildings and trees are often false positives. However, most existing pedestrian detectors ignore or inadequately utilize semantic context. In this paper, in order to make full use of the urban-scene semantics to facilitate pedestrian detection, we propose a new method called Semantical Modulation based Pedestrian Detector (SMPD). First, for efficiency, a semantic prediction module is jointly learned with a baseline detector for semantic predictions. Second, a semantic integration module is designed to exploit the urban-scene semantic context for detection. Specifically, we force it to be an independent detection branch based solely on semantic information. In this way, together with the baseline detector, the fused detection results explicitly depend on both the learned appearance features and the scene context around pedestrians. In addition, while existing methods cannot be applied to the datasets where semantic annotations are not available for training, we introduce a semi-supervised transfer learning approach to make our method suitable for more scenarios. We demonstrate experimentally that, thanks to the integration of semantic context from urban scenes, SMPD can accurately detect small and occluded pedestrians, as well as effectively remove false positives. As a result, SMPD achieves the new state of the art on the Citypersons and Caltech datasets. (c) 2021 Elsevier B.V. All rights reserved.
关键词	Pedestrian detection Semantic context Urban scene
DOI	10.1016/j.neucom.2021.11.091
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0100400] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61802407] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[62071466] ; National Natural Science Foundation of China[62076242]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000761694000001
出版者	ELSEVIER
七大方向——子方向分类	目标检测、跟踪与识别
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48024
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
通讯作者	Jiang, Hangzhi
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Incept Inst Artificial Intelligence IIAI, Abu Dhabi, U Arab Emirates
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Jiang, Hangzhi,Liao, Shengcai,Li, Jinpeng,et al. Urban scene based Semantical Modulation for Pedestrian Detection[J]. NEUROCOMPUTING,2022,474:1-12.
APA	Jiang, Hangzhi,Liao, Shengcai,Li, Jinpeng,Prinet, Veronique,&Xiang, Shiming.(2022).Urban scene based Semantical Modulation for Pedestrian Detection.NEUROCOMPUTING,474,1-12.
MLA	Jiang, Hangzhi,et al."Urban scene based Semantical Modulation for Pedestrian Detection".NEUROCOMPUTING 474(2022):1-12.