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Updated: Aug 28, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Detecting and tracking using 2D laser range finders and deep learning.

Eugenio Aguirre¹, Miguel García-Silvente¹

¹Department of Computer Science and A.I. (DECSAI). Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI). CITIC-UGR., University of Granada (UGR), 18071 Granada, Spain.

Neural Computing & Applications

|September 19, 2022

Summary

This summary is machine-generated.

This study introduces an automated method for labeling leg data using a mobile robot, camera, and 2D laser rangefinder (LRF). This approach significantly improves people detection and tracking accuracy with 2D LRFs.

Keywords:

2D laser Automatic labelling Deep learning Machine learning People detection and tracking

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Area of Science:

Robotics
Computer Vision
Machine Learning

Background:

Detecting and tracking people with 2D laser rangefinders (LRFs) is difficult due to leg motion, self-occlusion, and similar objects.
Existing methods rely on manually labeled datasets, which are time-consuming and labor-intensive.

Purpose of the Study:

To develop a robust and efficient 2D LRF-based people detector and tracker.
To create an automatically labeled dataset for training leg detection models.
To overcome the limitations of current state-of-the-art approaches.

Main Methods:

A mobile robot equipped with a calibrated monocular camera and 2D LRF was used.
Deep learning object detection identified people and leg keypoints in images.
Extrinsic calibration between the 2D laser and camera enabled automatic leg instance labeling.
A machine learning leg detector was trained on the automatically labeled dataset.
A Kalman filter-based people detection and tracking algorithm was developed and assessed.

Main Results:

The proposed system successfully generated an automatically labeled dataset for leg patterns.
The developed leg detector, trained on this dataset, achieved high accuracy.
The Kalman filter-based tracker demonstrated superior performance compared to the state-of-the-art Angus Leigh's detector and tracker.
Experimental validation confirmed the robustness and efficiency of the proposed system.

Conclusions:

Automatic labeling of leg data using a calibrated camera and 2D LRF is feasible and effective.
The proposed system offers a significant advancement in 2D LRF-based people detection and tracking.
This method provides a more efficient and accurate solution for mobile robot perception.