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Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection.

Dewen Wu1, Ruizhi Chen1,2, Yue Yu1

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China.

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Summary
This summary is machine-generated.

This study introduces a privacy-preserving passive visual positioning system. It achieves accurate indoor localization using pedestrian detection and projection transformation, overcoming active visual methods

Keywords:
passive visual positioningpedestrian detectionprojection transformationregion of interest attention model

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Area of Science:

  • Computer Vision
  • Robotics
  • Indoor Localization

Background:

  • Active visual positioning offers high accuracy but raises privacy concerns due to user-initiated photo capture.
  • Existing methods like Wi-Fi, CSI, and PDR often lack the precision of visual approaches.
  • There is a need for privacy-conscious indoor positioning solutions.

Purpose of the Study:

  • To develop a passive visual positioning system that enhances privacy.
  • To achieve high positioning accuracy without requiring users to capture images.
  • To provide a reliable indoor localization method for mobile platforms.

Main Methods:

  • A three-step approach: pretreatment (camera calibration/installation), pedestrian detection using deep convolutional neural networks with a region of interest attention model (RIAM), and pose estimation via projection transformation (PT).
  • Utilizes security cameras in non-private areas.
  • Leverages neighboring frame detection and map information for enhanced detection.

Main Results:

  • Experimental validation in a 100 sq meter hall with 41 test points.
  • Achieved a Root Mean Square Error (RMSE) of 0.48 m.
  • Demonstrated a 90% positioning error of 0.73 m.

Conclusions:

  • The proposed passive visual positioning method offers high accuracy and performance.
  • This system effectively addresses privacy concerns associated with active visual positioning.
  • It presents a viable alternative for indoor localization applications.