Real-Time Robot Vision on Low-Performance Computing Hardware

Gongjin Lan, Jesus Benito-Picazo, Diederik M. Roijers, Enrique Dominguez, A. E. Eiben

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Small robots have numerous interesting applications in domains like industry, education, scientific research, and services. For most applications vision is important, however, the limitations of the computing hardware make this a challenging task. In this paper, we address the problem of real-time object recognition and propose the Fast Regions of Interest Search (FROIS) algorithm to quickly find the ROIs of the objects in small robots with low-performance hardware. Subsequently, we use two methods to analyze the ROIs. First, we develop a Convolutional Neural Network on a desktop and deploy it onto the low-performance hardware for object recognition. Second, we adopt the Histogram of Oriented Gradients descriptor and linear Support Vector Machines classifier and optimize the HOG component for faster speed. The experimental results show that the methods work well on our small robots with Raspberry Pi 3 embedded 1.2 GHz ARM CPUs to recognize the objects. Furthermore, we obtain valuable insights about the trade-offs between speed and accuracy.

Original languageEnglish
Title of host publication15th International Conference on Control, Automation, Robotics and Vision (ICARCV 2018)
Subtitle of host publication[Proceedings]
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Number of pages7
ISBN (Electronic)9781538695821
ISBN (Print)9781538695838
Publication statusPublished - 2018
Event15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018 - Singapore, Singapore
Duration: 18 Nov 201821 Nov 2018


Conference15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018


  • Real-time
  • Robot Vision
  • Low-performance Computing Hardware
  • Object recognition
  • SVM
  • Raspberry Pi
  • Computer vision


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