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Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based

Alexandra Ștefania Ghiță1, Adina Magda Florea1

  • 1Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania.

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Summary
This summary is machine-generated.

This study introduces a real-time people re-identification system that combines environmental semantics and social influence for accurate trajectory prediction. The system enhances human-robot interaction and safety in autonomous driving, improving performance by over 5%.

Keywords:
computer visionmachine learningpedestrian trackingperson re-identification and trackingsocial roboticstrajectory prediction

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Human-Robot Interaction

Background:

  • Autonomous systems like social robots and self-driving cars require robust human re-identification for safe and effective operation.
  • Real-time tracking and trajectory prediction are critical for applications demanding quick reactions, such as autonomous driving.

Purpose of the Study:

  • To develop and evaluate a novel real-time people re-identification system.
  • To enhance human-robot interaction by accurately tracking individuals in dynamic environments.
  • To improve safety in autonomous driving through precise people trajectory prediction.

Main Methods:

  • A real-time people re-identification system integrating semantic environmental information and social influence for trajectory prediction.
  • Evaluation within social robotics (using the AMIRO framework) and autonomous driving scenarios.
  • Quantitative and qualitative analysis using existing datasets and real-time acquired data.

Main Results:

  • The proposed system demonstrates improved performance in people re-identification and trajectory prediction.
  • An enhancement of over 5% in the Multiple Object Tracking Accuracy (MOTA) metric was achieved compared to existing modules.
  • The system proved effective in both social robotics and autonomous driving case studies.

Conclusions:

  • The combined approach of semantic and social information significantly improves people re-identification and trajectory prediction.
  • The developed system offers a viable solution for enhancing safety and interaction in autonomous systems.
  • The method shows promise for real-world applications in social robotics and autonomous vehicles.