Visual-GPS: Ego-Downward and Ambient Video based Person Location Association
文献类型：会议论文
| 作者 | Yang L(杨亮)1,2 ; Jiang, Hao4; Huo, Zhouyuan3
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| 出版日期 | 2019 |
| 会议日期 | June 16-20, 2019 |
| 会议地点 | Long Beach, CA |
| 页码 | 371-380 |
| 英文摘要 | In a crowded and cluttered environment, identifying a particular person is a challenging problem. Current identification approaches are not able to handle the dynamic environment. In this paper, we tackle the problem of identifying and tracking a person of interest in the crowded environment using egocentric and third person view videos. We propose a novel method (Visual-GPS) to identify, track, and localize the person, who is capturing the egocentric video, using joint analysis of imagery from both videos. The output of our method is the bounding box of the target person detected in each frame of the third person view and the 3D metric trajectory. At glance, the views of the two cameras are quite different. This paper illustrates an insight into how they are correlated. Our proposed method uses several difference clues. In addition to using RGB images, we take advantage of both the body motion and action features to correlate the two views. We can track and localize the person by finding the most correlated individual in the third view. Furthermore, the target person's 3D trajectory is recovered based on the mapping of the 2d-3D body joints. Our experiment confirms the effectiveness of ETVIT network and shows 18.32% improvement in detection accuracy against the baseline methods. |
| 产权排序 | 1 |
| 会议录 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2019)
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2160-7508 |
| ISBN号 | 978-1-7281-2506-0 |
| WOS记录号 | WOS:000569983600044 |
| 源URL | [http://ir.sia.cn/handle/173321/27662]  |
| 专题 | 工艺装备与智能机器人研究室 |
| 通讯作者 | Yang L(杨亮) |
| 作者单位 | 1.Robotics Lab, The City College of New York, City University, New York, USA 2.State Key Laboratory of Robotics, University of Chinese Academy of Sciences, China 3.University of Pittsburgh, Pittsburgh, USA 4.Microsoft, Redmond, USA |
| 推荐引用方式 GB/T 7714 | Yang L,Jiang, Hao,Huo, Zhouyuan. Visual-GPS: Ego-Downward and Ambient Video based Person Location Association[C]. 见:. Long Beach, CA. June 16-20, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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