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Unified Optimization for Multiple Active Object Recognition Tasks with Feature Decision Tree

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Abstract

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.

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Code Availability

Code generated or used during the study is available from the corresponding author by request.

Funding

This work is supported by the National Natural Science Foundation of China under Grant no. U1713216.

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Authors and Affiliations

  1. Faculty of Robot Science and Engineering, Northeastern University, Shenyang, 110169, China

    Haibo Sun, Chenglong Xu & Jianyu Wang

  2. Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, 110016, China

    Haibo Sun, Feng Zhu, Yingming Hao, Shuangfei Fu, Yanzi Kong & Jianyu Wang

  3. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Haibo Sun, Feng Zhu, Yingming Hao, Shuangfei Fu, Yanzi Kong & Jianyu Wang

  4. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

    Haibo Sun, Feng Zhu, Yingming Hao, Shuangfei Fu, Yanzi Kong & Jianyu Wang

  5. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yanzi Kong

Authors
  1. Haibo Sun
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  2. Feng Zhu
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  3. Yingming Hao
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  4. Shuangfei Fu
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  5. Yanzi Kong
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  6. Chenglong Xu
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  7. Jianyu Wang
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Contributions

Haibo Sun, Feng Zhu and Yingming Hao conceived and designed the approach. Yanzi Kong strongly contributed in the construction of simulation and real experimental environment. Shuangfei Fu helped with code implementation, data collection, and experimentation. The first draft of the manuscript was written by Haibo Sun. Chenglong Xu and Jianyu Wang thoroughly corrected the manuscript. All authors read and approved the final manuscript.

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Correspondence to Haibo Sun or Feng Zhu.

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Sun, H., Zhu, F., Hao, Y. et al. Unified Optimization for Multiple Active Object Recognition Tasks with Feature Decision Tree. J Intell Robot Syst 103, 31 (2021). https://doi.org/10.1007/s10846-021-01488-x

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  • DOI: https://doi.org/10.1007/s10846-021-01488-x

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