Modeling Mutual Visibility Relationship in Pedestrian Detection
文献类型：会议论文
| 作者 | Wanli Ouyang; Xingyu Zeng; Xiaogang Wang |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：深圳先进技术研究院
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