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	Modeling Mutual Visibility Relationship in Pedestrian Detection
	Wanli Ouyang; Xingyu Zeng; Xiaogang Wang
	2013
会议名称	26th IEEE Conference on Computer Vision and Pattern Recognition
会议地点	Portland, OR, United states
英文摘要	Detecting pedestrians in cluttered scenes is a challenging problem in computer vision. The difficulty is added when several pedestrians overlap in images and occlude each other. We observe, however, that the occlusion/visibility statuses of overlapping pedestrians provide useful mutual relationship for visibility estimation - the visibility estimation of one pedestrian facilitates the visibility estimation of another. In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians. The visibility relationship among pedestrians is learned from the deep model for recognizing co-existing pedestrians. Experimental results show that the mutual visibility deep model effectively improves the pedestrian detection results. Compared with existing image-based pedestrian detection approaches, our approach has the lowest average miss rate on the Caltech-Train dataset, the Caltech-Test dataset and the ETH dataset. Including mutual visibility leads to 4% - 8% improvements on multiple benchmark datasets.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/4478]
专题	深圳先进技术研究院_集成所
作者单位	2013
推荐引用方式 GB/T 7714	Wanli Ouyang,Xingyu Zeng,Xiaogang Wang. Modeling Mutual Visibility Relationship in Pedestrian Detection[C]. 见:26th IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, United states.

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