It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
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/
Cite
Text
Mangalam et al. "It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58536-5_45Markdown
[Mangalam et al. "It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/mangalam2020eccv-journey/) doi:10.1007/978-3-030-58536-5_45BibTeX
@inproceedings{mangalam2020eccv-journey,
title = {{It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction}},
author = {Mangalam, Karttikeya and Girase, Harshayu and Agarwal, Shreyas and Lee, Kuan-Hui and Adeli, Ehsan and Malik, Jitendra and Gaidon, Adrien},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58536-5_45},
url = {https://mlanthology.org/eccv/2020/mangalam2020eccv-journey/}
}