"Seeing Is Believing": Pedestrian Trajectory Forecasting Using Visual Frustum of Attention
Abstract
In this paper we show the importance of the head pose estimation in the task of trajectory forecasting. This cue, when produced by an oracle and injected in a novel socially-based energy minimization approach, allows to get state-of-the-art performances on four different forecasting benchmarks, without relying on additional information such as expected destination and desired speed, which are supposed to be know beforehand for most of the current forecasting techniques. Our approach uses the head pose estimation for two aims: 1) to define a view frustum of attention, highlighting the people a given subject is more interested about, in order to avoid collisions; 2) to give a shorttime estimation of what would be the desired destination point. Moreover, we show that when the head pose estimation is given by a real detector, though the performance decreases, it still remains at the level of the top score forecasting systems.
Cite
Text
Hasan et al. ""Seeing Is Believing": Pedestrian Trajectory Forecasting Using Visual Frustum of Attention." IEEE/CVF Winter Conference on Applications of Computer Vision, 2018. doi:10.1109/WACV.2018.00134Markdown
[Hasan et al. ""Seeing Is Believing": Pedestrian Trajectory Forecasting Using Visual Frustum of Attention." IEEE/CVF Winter Conference on Applications of Computer Vision, 2018.](https://mlanthology.org/wacv/2018/hasan2018wacv-seeing/) doi:10.1109/WACV.2018.00134BibTeX
@inproceedings{hasan2018wacv-seeing,
title = {{"Seeing Is Believing": Pedestrian Trajectory Forecasting Using Visual Frustum of Attention}},
author = {Hasan, Irtiza and Setti, Francesco and Tsesmelis, Theodore and Del Bue, Alessio and Cristani, Marco and Galasso, Fabio},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2018},
pages = {1178-1185},
doi = {10.1109/WACV.2018.00134},
url = {https://mlanthology.org/wacv/2018/hasan2018wacv-seeing/}
}