It is not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli, Jitendra Malik, Adrien Gaidon
Abstract
Human trajectory forecasting with multiple socially interact-ing 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%. Code available at the home page: https://karttikeya.github.io/publication/htf/"
BibTeX
@inproceedings{eccv2020_itisnotthejourne,
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},
booktitle = {ECCV 2020},
year = {2020}
}