Online RGB-D tracking via detection-learning-segmentation

doi:10.1109/ICPR.2016.7899805

	Online RGB-D tracking via detection-learning-segmentation
	An, Ning; Zhao, Xiao-Guang; Hou, Zeng-Guang
	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
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.
DOI	10.1109/ICPR.2016.7899805
收录类别	EI
引用统计
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14559
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	An, Ning
作者单位	Institute of Automation Chinese Academy of Sciences
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	An, Ning,Zhao, Xiao-Guang,Hou, Zeng-Guang. Online RGB-D tracking via detection-learning-segmentation[C],2016.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Online RGB-D trackin（1690KB）	会议论文		开放获取	CC BY-NC-SA	浏览下载