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Advancing Point Cloud Perception: A Focus on People Detection.

Assia Belbachir1, Antonio M Ortiz1, Atle Aalerud1

  • 1NORCE Research AS, Grimstad, Norway.

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Summary
This summary is machine-generated.

This study introduces a Random Forest Classifier (RFC) for efficient human detection in LiDAR point clouds, overcoming challenges like data sparsity and occlusions for real-time applications.

Keywords:
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Area of Science:

  • Computer Vision
  • Robotics
  • 3D Data Analysis

Background:

  • Point-cloud data is crucial for 3D scene analysis.
  • Real-time human detection in point clouds faces challenges due to sparsity, irregular sampling, and occlusions.

Purpose of the Study:

  • To develop an efficient people detection pipeline for high-resolution LiDAR point clouds.
  • To address the limitations of current methods in real-time human detection.

Main Methods:

  • Utilized a feature-engineered pipeline with a Random Forest Classifier (RFC).
  • Implemented a ground-removal algorithm using region growing.
  • Developed a compact feature set comprising 15 geometric and intensity-based descriptors.

Main Results:

  • The RFC approach demonstrated good performance in human detection tasks.
  • Comparative analysis showed effectiveness against MLP and PointNet baselines.
  • Evaluated using comprehensive metrics on two distinct datasets.

Conclusions:

  • The proposed RFC pipeline is practically applicable for real-time, on-device human detection.
  • The feature-engineered approach effectively handles challenges in LiDAR point-cloud data.
  • This method offers a viable solution for robust human detection in 3D environments.