InhwanBae/EigenTrajectory
Official Code for "EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting (ICCV 2023)"
This project helps improve the accuracy of predicting where pedestrians will move next. It takes historical movement data of people and generates a compact representation of their motion, which then feeds into existing prediction models. The output is a significantly more accurate and reliable forecast of future pedestrian paths, making it useful for urban planners, autonomous vehicle developers, or crowd management professionals.
106 stars. No commits in the last 6 months.
Use this if you need to improve the prediction accuracy of existing pedestrian trajectory forecasting models using a more efficient data representation.
Not ideal if you are looking for a completely new trajectory prediction model from scratch, as this project focuses on enhancing existing ones.
Stars
106
Forks
8
Language
Python
License
MIT
Category
Last pushed
Jun 30, 2025
Commits (30d)
0
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