Unified Optimization for Multiple Active Object Recognition Tasks with Feature Decision Tree | |
Sun HB(孙海波)2,3,4,5; Zhu F(朱枫)2,3,4; Hao YM(郝颖明)2,3,4; Fu SF(付双飞)2,3,4; Kong YZ(孔研自)1,2,3,4; Xu CL(徐成龙)5; Wang JY(王健宇)2,3,4,5 | |
刊名 | JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS |
2021 | |
卷号 | 103期号:2页码:1-15 |
关键词 | Active object recognition Prior feature distribution table Feature decision tree Next best viewpoint |
ISSN号 | 0921-0296 |
产权排序 | 1 |
英文摘要 | Visual object recognition plays an important role in the fields of computer vision and robotics. Static analysis of an image from a single viewpoint may not contain enough information to recognize an object unambiguously. Active object recognition (AOR) is aimed at collecting additional information to reduce ambiguity by purposefully adjusting the viewpoint of an observer. Existing AOR methods are oriented to a single task whose goal is to recognize an object by the minimum number of viewpoints. This paper presents a novel framework to deal with multiple AOR tasks based on feature decision tree (FDT). In the framework, in the light of the distribution of predetermined features on each object in a model base, a prior feature distribution table is firstly created as a kind of prior knowledge. Then it is utilized for the construction of FDT which describes the transition process of recognition states when different viewpoints are selected. Finally, in order to determine the next best viewpoints for the tasks with different goals, a unified optimization problem is established and solved by tree dynamic programming algorithm. In addition, the existing evaluation method of viewpoint planning (VP) efficiency is improved. According to whether the prior probability of the appearance of each object is known, the VP efficiency of different tasks is evaluated respectively. Experiments on the simulation and real environment show that the proposed framework obtains rather promising results in different AOR tasks. |
资助项目 | National Natural Science Foundation of China[U1713216] |
WOS关键词 | VIEWPOINT SELECTION ; VIEW |
WOS研究方向 | Computer Science ; Robotics |
语种 | 英语 |
WOS记录号 | WOS:000695834700001 |
资助机构 | National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [U1713216] |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/29659] |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Sun HB(孙海波); Zhu F(朱枫) |
作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China 5.Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China |
推荐引用方式 GB/T 7714 | Sun HB,Zhu F,Hao YM,et al. Unified Optimization for Multiple Active Object Recognition Tasks with Feature Decision Tree[J]. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS,2021,103(2):1-15. |
APA | Sun HB.,Zhu F.,Hao YM.,Fu SF.,Kong YZ.,...&Wang JY.(2021).Unified Optimization for Multiple Active Object Recognition Tasks with Feature Decision Tree.JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS,103(2),1-15. |
MLA | Sun HB,et al."Unified Optimization for Multiple Active Object Recognition Tasks with Feature Decision Tree".JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 103.2(2021):1-15. |
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