Abstract

Intelligent robots such as domestic service robots (DSR), office robots are required to be able to interact with dynamic and complex environments. In order to carry out the tasks given in such environments, the ability to interact with the objects becomes prevalent. In particular, the DSR need to interact with a household object that is normally being lied in arbitrary positions at the home. To accomplish such a challenging task, the robot has to be able to recognize the object. As human does, a visual-based recognition is most common and natural for intelligent robots. To realize such ability the use of visual information captured from a visual sensor is necessary. Thanks to the second version of Microsoft Kinect (Kinect V2), visual information such as color, depth, and near-infrared information can be acquired. In this study, the captured visual information is then processed for object extraction and object recognition. To solve the problems, we propose a method that exploits multiple features such as color and shape features. The proposed method has incorporated the results of each classifier such as k-nearest neighbor (kNN) using a simple probabilistic method to obtain robust recognition results of household objects. To validate the proposed method, we have conducted several experiments. The results reveal that our method can achieve an accuracy of (84.02 ± 18.85) % for the recognition of household objects with extreme conditions.

Original languageEnglish
Title of host publicationProceedings - 8th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2021
EditorsAuzani Jiddin, M Amjad, Imam MI Subroto, Mochammad Facta
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages169-174
Number of pages6
ISBN (Electronic)9786236264195
DOIs
Publication statusPublished - 2021
Event8th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2021 - Virtual, Semarang, Indonesia
Duration: 20 Oct 202121 Oct 2021

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Volume2021-October
ISSN (Print)2407-439X

Conference

Conference8th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2021
Country/TerritoryIndonesia
CityVirtual, Semarang
Period20/10/2121/10/21

Keywords

  • Color feature
  • Household objects
  • Object recognition
  • Probabilistic method
  • Shape feature

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