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Research on Object Detection and Tracking Methods for aLow-Speed Mobile Platform.

Gang Liu^1,2, Tao Jiang¹, Ming Ye¹

¹Automotive Parts Advanced Manufacturing Technology Education Key Laboratory, Chongqing University of Technology, Chongqing 400054, China.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This study introduces a novel system combining Ultra-Wideband (UWB) and gait analysis for precise autonomous following. The integrated approach enhances positioning accuracy in challenging indoor environments.

Keywords:

Extended Kalman Filter (EKF)Model Predictive Control (MPC)Ultra-Wideband (UWB)gait feature recognition multi-sensor fusion trajectory tracking

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: January 18, 2020

Area of Science:

Robotics
Computer Vision
Sensor Fusion

Background:

Autonomous following systems face challenges in dynamic indoor environments due to occlusion and interference.
Existing positioning systems struggle with accuracy and stability in complex settings.

Purpose of the Study:

To enhance the positioning stability and accuracy of autonomous following systems.
To develop a robust system for leader trajectory estimation and tracking in indoor environments.

Main Methods:

Implemented an Ultra-Wideband (UWB) positioning network using an optimized Double-Sided Two-Way Ranging (DS-TWR) protocol.
Integrated computer vision-based gait analysis (GaitPart framework) to address Non-Line-of-Sight (NLOS) UWB limitations.
Utilized an Extended Kalman Filter (EKF) to fuse UWB and gait data for trajectory estimation, tracked by a Model Predictive Controller (MPC).

Main Results:

The fused UWB and gait analysis system achieved precise and robust leader trajectory estimation.
Tracking deviation remained within 50 mm for most trajectory points.
Maximum deviation was 115 mm during turns, demonstrating strong robustness.

Conclusions:

The proposed UWB and gait analysis fusion method significantly improves autonomous following system performance.
The system exhibits strong robustness and practical utility for intelligent vehicle applications.
This approach offers a viable solution for accurate positioning in challenging indoor environments.