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题名:
An Occlusion-Aware Framework for Real-Time 3D Pose Tracking
作者: Fu ML(付明亮)1,2; Leng YQ(冷雨泉)3; Luo HT(骆海涛)1; Zhou WJ(周维佳)1
作者部门: 空间自动化技术研究室
通讯作者: Fu ML(付明亮)
关键词: pose tracking ; occlusion handling ; online rendering ; motion compensation
刊名: Sensors (Switzerland)
ISSN号: 1424-8220
出版日期: 2018
卷号: 18, 期号:8, 页码:1-20
产权排序: 1
项目资助者: National Science Foundation of China under Grant 51505470
摘要: Random forest-based methods for 3D temporal tracking over an image sequence have gained increasing prominence in recent years. They do not require object’s texture and only use the raw depth images and previous pose as input, which makes them especially suitable for textureless objects. These methods learn a built-in occlusion handling from predetermined occlusion patterns, which are not always able to model the real case. Besides, the input of random forest is mixed with more and more outliers as the occlusion deepens. In this paper, we propose an occlusion-aware framework capable of real-time and robust 3D pose tracking from RGB-D images. To this end, the proposed framework is anchored in the random forest-based learning strategy, referred to as RFtracker. We aim to enhance its performance from two aspects: integrated local refinement of random forest on one side, and online rendering based occlusion handling on the other. In order to eliminate the inconsistency between learning and prediction of RFtracker, a local refinement step is embedded to guide random forest towards the optimal regression. Furthermore, we present an online rendering-based occlusion handling to improve the robustness against dynamic occlusion. Meanwhile, a lightweight convolutional neural network-based motion-compensated (CMC) module is designed to cope with fast motion and inevitable physical delay caused by imaging frequency and data transmission. Finally, experiments show that our proposed framework can cope better with heavily-occluded scenes than RFtracker and preserve the real-time performance.
语种: 英语
内容类型: 期刊论文
URI标识: http://210.72.131.170/handle/173321/22457
Appears in Collections:空间自动化技术研究室_期刊论文

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作者单位: 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China

Recommended Citation:
Fu ML,Leng YQ,Luo HT,et al. An Occlusion-Aware Framework for Real-Time 3D Pose Tracking[J]. Sensors (Switzerland),2018,18(8):1-20.
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