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A novel fast pedestrian recognition algorithm based on point cloud compression and boundary extraction.

Yanjun Zhang1

  • 1Zhong Shan Polytechnic, Zhongshan, China.

Peerj. Computer Science
|June 22, 2023
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Summary
This summary is machine-generated.

This study introduces a new method for compressing laser radar point cloud data, significantly improving pedestrian recognition accuracy in autonomous driving systems. The approach enhances data compression while retaining crucial features for faster and more reliable detection.

Keywords:
Boundary extractionDriverlessPedestrian identificationPoint cloud data compression

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Data Processing

Background:

  • Autonomous driving relies on accurate pedestrian recognition for safety.
  • Laser radar provides high-resolution 3D point cloud data but faces challenges with massive data size and redundancy.
  • Efficient processing of point cloud data is crucial for real-time applications like path planning and obstacle avoidance.

Purpose of the Study:

  • To develop a fast pedestrian recognition algorithm using fused laser radar point cloud data.
  • To propose an effective point cloud data compression method to address challenges in transmission, storage, and processing speed.
  • To enhance the accuracy and efficiency of pedestrian detection in autonomous driving systems.

Main Methods:

  • Utilized fused point cloud data from laser radar for pedestrian recognition.
  • Developed a point cloud data compression technique based on feature point extraction and reduced voxel grid.
  • Employed an image mapping-based algorithm for pedestrian recognition on compressed point cloud data.

Main Results:

  • The proposed compression algorithm improved the peak signal-to-noise ratio by 6.02%.
  • Recognition accuracy saw significant improvements: 16.93% (simple scenes), 17.2% (medium scenes), and 16.12% (difficult scenes).
  • Outperformed random sampling compression methods in terms of accuracy and feature retention.

Conclusions:

  • The proposed method achieves superior data compression for laser radar point clouds.
  • Key feature points are effectively retained in the compressed data, ensuring recognition quality.
  • The compressed point cloud data enables faster and more accurate pedestrian recognition for autonomous driving applications.