It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction

Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli +2 more
4/4/2020
cs.CVcs.LG

Official code: HarshayuGirase/Human-Path-Prediction (409 stars, MIT, last commit 2022-03-30).

Abstract

Human trajectory forecasting with multiple socially interacting agents is of critical importance for autonomous navigation in human environments, e.g., for self-driving cars and social robots. In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction. PECNet infers distant trajectory endpoints to assist in long-range multi-modal trajectory prediction. A novel non-local social pooling layer enables PECNet to infer diverse yet socially compliant trajectories. Additionally, we present a simple "truncation-trick" for improving few-shot multi-modal trajectory prediction performance. We show that PECNet improves state-of-the-art performance on the Stanford Drone trajectory prediction benchmark by ~20.9% and on the ETH/UCY benchmark by ~40.8%. Project homepage: https://karttikeya.github.io/publication/htf/

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Code Implementations(1)

HarshayuGirase/Human-Path-PredictionOfficial100%
MIT

State-of-the-art methods for human trajectory forecasting. Contains code for papers published at ECCV 2020 and ICCV 2021.

40986Python, Jupyter NotebookJun 19, 20204 years agoMIT

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Cite this paper

@article{mangalam2020journey,
  title  = {It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction},
  author = {Karttikeya Mangalam and Harshayu Girase and Shreyas Agarwal and Kuan-Hui Lee and Ehsan Adeli and Jitendra Malik and Adrien Gaidon},
  year   = {2020},
  eprint = {2004.02025},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2004.02025}
}

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