Keywords: Trajectory Prediction
TL;DR: Reproducibility report for the paper (From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting)
Abstract: Human trajectory forecasting is an inherenty multimodal problem. Uncertainty in future trajectories stems from two sources: (a) sources that are known to the agent but unknown to the model, such as long term goals and (b) sources that are unknown to both the agent & the model, such as intent of other agents & irreducible randomness in decisions. This stochasticity is modelled in two major ways: the epistemic uncertainity which accounts for the multimodal nature of the long term goals and the aleatoric uncertainity which accounts for the multimodal nature of the waypoints. Furthermore, the paper extends the existing prediction horizon to up to a minute. The aforementioned features are encompassed into Y-Net, a scene compliant trajectory forecasting network. The network has been implemented on the following datasets : (a) Stanford Drone (SDD) (b) ETH/UCY (c) Intersection Drone. The network significantly improves upon state-of-the-art performance for both short and long prediction horizon settings.
Paper Url: https://arxiv.org/abs/2012.01526
Paper Venue: ICCV 2021
Supplementary Material: zip
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2012.01526/code)