Online RGB-D tracking via detection-learning-segmentation
文献类型：会议论文
| 作者 | An, Ning ; Zhao, Xiao-Guang; Hou, Zeng-Guang
|
| 出版日期 | 2016-12 |
| 会议名称 | Pattern Recognition (ICPR), 2016 23rd International Conference on |
| 会议日期 | 4-8 Dec. 2016 |
| 会议地点 | Cancun, Mexico |
| 通讯作者 | An, Ning |
| 英文摘要 | In this paper, we address the problem of online RGB-D tracking where the target object undergoes significant appearance changes. To sufficiently exploit the color and depth cues, we propose a novel RGB-D tracking framework (DLS) that simultaneously builds the target 2D appearance model and 3D distribution model. The framework decomposes the tracking task into detection, learning and segmentation. The detection and segmentation components locate the target collaboratively by using the two target models. An adaptive depth histogram is proposed in the segmentation component to efficiently locate the target in depth frames. The learning component estimates the detection and segmentation errors, updates the target models from the most confident frames by identifying two kinds of distractors: potential failure and occlusion. Extensive experimental results on a large-scale benchmark dataset show that the proposed method performs favourably against state-of-the-art RGB-D trackers in terms of efficiency, accuracy, and robustness. |
| 收录类别 | EI |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14559]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 作者单位 | Institute of Automation Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | An, Ning,Zhao, Xiao-Guang,Hou, Zeng-Guang. Online RGB-D tracking via detection-learning-segmentation[C]. 见:Pattern Recognition (ICPR), 2016 23rd International Conference on. Cancun, Mexico. 4-8 Dec. 2016. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。