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Updated: Jul 1, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Pedestrian navigation activity recognition method based on two-stream transformer and contrastive learning.

Qu Wang^1,2, Junying Ma¹, Meixia Fu^1,2

¹School of Automation Science and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

|April 23, 2026

Summary

This study introduces a novel method for pedestrian navigation activity recognition (PNAR) using a two-stream convolutional transformer with self-supervised learning. The approach achieves high accuracy and superior generalization across diverse datasets.

Keywords:

computer science engineering

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Area of Science:

Computer Science
Robotics
Signal Processing

Background:

Pedestrian navigation activity recognition (PNAR) is crucial for pedestrian positioning and navigation.
Existing methods face challenges in learning robust and generalizable sensor data representations.

Purpose of the Study:

To propose a novel PNAR method combining a two-stream convolutional transformer with self-supervised contrastive pretraining.
To enhance the robustness, transferability, and generalizability of PNAR models.

Main Methods:

A two-stream architecture: spatial stream for multi-modal sensor dependencies and temporal stream for temporal relationships using attention.
Self-supervised contrastive pretraining on unlabeled data to learn invariant representations.
Evaluation on four public datasets and cross-dataset experiments.

Main Results:

Achieved 99.08% accuracy and 99.22% F1-score, outperforming existing state-of-the-art methods.
Demonstrated superior generalization ability in cross-dataset experiments with varying sensor configurations and activity labels.

Conclusions:

The proposed two-stream convolutional transformer with self-supervised pretraining is effective for PNAR.
The method significantly improves generalization, making it suitable for diverse real-world applications.