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Related Experiment Video

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LidPose: Real-Time 3D Human Pose Estimation in Sparse Lidar Point Clouds with Non-Repetitive Circular Scanning

Lóránt Kovács1,2, Balázs M Bódis1,2, Csaba Benedek1,2

  • 1HUN-REN Institute for Computer Science and Control (SZTAKI), Kende utca 13-17, H-1111 Budapest, Hungary.

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|June 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces LidPose, a novel vision-transformer method for real-time human skeleton estimation using lidar point clouds. It effectively addresses lidar data sparsity for improved surveillance applications.

Keywords:
NRCS lidarlidar-only 3D human pose estimationpoint cloudreal-time surveillancerosetta pattern non-repetitive circular scanning

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Human pose estimation is crucial for surveillance and human-computer interaction.
  • Existing methods struggle with sparse and uniquely patterned lidar data, particularly from Non-Repetitive Circular Scanning (NRCS) sensors.
  • Balancing spatial and temporal resolution is key for analyzing NRCS lidar data.

Purpose of the Study:

  • To develop a novel, end-to-end pose estimation method for real-time human skeleton detection in NRCS lidar point clouds.
  • To adapt existing vision-transformer architectures to handle the sparsity and scanning patterns of NRCS lidars.
  • To create a comprehensive dataset for evaluating lidar-based human perception.

Main Methods:

  • Proposed LidPose, a vision-transformer-based method building on ViTPose.
  • Introduced adaptations to handle NRCS lidar data sparsity and scanning patterns.
  • Implemented foreground/background segmentation for region of interest (RoI) selection.
  • Utilized raw NRCS lidar measurement sequences for moving pedestrian detection and skeleton fitting.

Main Results:

  • LidPose demonstrates effective real-time human skeleton estimation from NRCS lidar data.
  • The method successfully addresses the challenges of data sparsity and scanning patterns.
  • A novel, real-world, multi-modal dataset with 2D/3D skeleton ground truth was created for evaluation.

Conclusions:

  • LidPose offers a robust solution for real-time human skeleton estimation using NRCS lidar.
  • The developed method and dataset advance the field of lidar-based perception for surveillance.
  • This work paves the way for improved pedestrian detection and analysis in challenging environments.