luigifilippochiara/Goal-SAR
Official code for the Paper "Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction", CVPRW 2022
This project helps traffic planners, urban designers, or crowd management professionals predict the future paths of pedestrians. By analyzing past movement data, it forecasts where people or vehicles are likely to go next, providing outputs that can inform safety assessments, facility planning, or simulation studies. The primary users are researchers or practitioners involved in analyzing crowd dynamics or traffic flow.
No commits in the last 6 months.
Use this if you need to accurately predict pedestrian or vehicle trajectories based on historical movement data, especially in complex, goal-oriented scenarios.
Not ideal if you're looking for a simple, out-of-the-box application without any need for data preparation or computational resources.
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Language
Python
License
MIT
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Last pushed
Apr 07, 2023
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