MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
Abstract
Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures. This paper shows that adding vislets, that is, short sequences of head pose estimations, allows to increase significantly the trajectory forecasting performance. We then propose to use vislets in a novel framework called MX-LSTM, capturing the interplay between tracklets and vislets thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. At the same time, MX-LSTM predicts the future head poses, increasing the standard capabilities of the long-term trajectory forecasting approaches. With standard head pose estimators and an attentional-based social pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic margin when the pedestrians slow down, a case where most of the forecasting approaches struggle to provide an accurate solution.
Cite
@article{arxiv.1805.00652,
title = {MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses},
author = {Irtiza Hasan and Francesco Setti and Theodore Tsesmelis and Alessio Del Bue and Fabio Galasso and Marco Cristani},
journal= {arXiv preprint arXiv:1805.00652},
year = {2018}
}
Comments
10 pages, 3 figures to appear in CVPR 2018