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NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences.

Diwei Sheng1, Yuxiang Chai1, Xinru Li1

  • 1New York University, Brooklyn, NY 11201, USA.

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|August 24, 2022
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Summary
This summary is machine-generated.

Visual place recognition (VPR) is more challenging with side views than front views. Data anonymization has minimal impact on VPR performance in urban environments.

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Visual place recognition (VPR) is essential for autonomous driving and navigation for the visually impaired.
  • Large-scale VPR systems face challenges including varied image view directions and privacy concerns from urban data.
  • The impact of these factors on VPR performance is not well understood.

Purpose of the Study:

  • To investigate the influence of image view direction and data anonymization on VPR performance.
  • To introduce the NYU-VPR dataset for studying these VPR challenges.
  • To provide benchmark results for popular VPR algorithms.

Main Methods:

  • Creation of the NYU-VPR dataset with over 200,000 images from a 2km×2km area near NYU, captured throughout 2016.
  • Evaluation of several popular VPR algorithms on the dataset.
  • Analysis of performance variations based on image view direction (front vs. side) and data anonymization.

Main Results:

  • Side views present significantly greater challenges for current VPR algorithms compared to front views.
  • Data anonymization demonstrated a negligible impact on VPR performance.
  • The study provides explanations and in-depth analysis for these observed performance variations.

Conclusions:

  • VPR systems need to be optimized for different view directions, with side views requiring further research.
  • Privacy-preserving techniques like data anonymization do not significantly hinder VPR accuracy in metropolitan settings.
  • The NYU-VPR dataset serves as a valuable resource for advancing VPR research.