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Orthogonal Trajectories01:26

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Related Experiment Video

Updated: May 20, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Dual-view diffusion for pedestrian trajectory imputation.

Pranav Singh Chib1, Pravendra Singh1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India.

Neural Networks : the Official Journal of the International Neural Network Society
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Synchronous Dual-View Diffusion, a novel method for trajectory imputation. The model effectively forecasts missing trajectory data, outperforming existing methods in pedestrian trajectory benchmarks.

Keywords:
Diffusion modelTrajectory imputationTrajectory prediction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Trajectory data often contains missing values due to sensor issues or occlusions.
  • Accurate trajectory imputation is crucial for improving trajectory prediction models.
  • Existing research on trajectory imputation is limited.

Purpose of the Study:

  • To propose a novel score-based diffusion model for effective trajectory imputation.
  • To enhance the performance of trajectory prediction by accurately filling missing trajectory coordinates.

Main Methods:

  • Introduced Synchronous Dual-View Diffusion (SyncDVD), a score-based diffusion model.
  • Leveraged a dual-view approach, conditioning the diffusion model on two synchronized perspectives of the observed trajectory.
  • Incorporated similarity constraints to maximize alignment between dual-view predictions for improved imputation consistency.

Main Results:

  • SyncDVD demonstrated consistent improvements in imputation performance across benchmark datasets.
  • Achieved superior results compared to existing trajectory imputation methods on pedestrian trajectory datasets.
  • Reported significant reductions in Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to the second-best method.

Conclusions:

  • The proposed Synchronous Dual-View Diffusion model effectively addresses missing values in trajectories.
  • The dual-view conditioning and similarity constraints enhance imputation accuracy and consistency.
  • SyncDVD offers a promising solution for improving trajectory imputation and subsequent trajectory prediction tasks.