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Deep learning based Person Re-identification with RGB-D sensors

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(3), pp.35-42
  • DOI : 10.9708/jksci.2021.26.03.035
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 13, 2021
  • Accepted : February 19, 2021
  • Published : March 31, 2021

Min-Kim 1 PARK DONGHYUN 1

1인하대학교

Accredited

ABSTRACT

In this paper, we propose a deep learning-based person re-identification method using a three-dimensional RGB-Depth Xtion2 camera considering joint coordinates and dynamic features(velocity, acceleration). The main idea of the proposed identification methodology is to easily extract gait data such as joint coordinates, dynamic features with an RGB-D camera and automatically identify gait patterns through a self-designed one-dimensional convolutional neural network classifier(1D-ConvNet). The accuracy was measured based on the F1 Score, and the influence was measured by comparing the accuracy with the classifier model (JC) that did not consider dynamic characteristics. As a result, our proposed classifier model in the case of considering the dynamic characteristics(JCSpeed) showed about 8% higher F1-Score than JC.

Citation status

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