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.

本文提出了一个新的轨迹预测框架 MX-LSTM，它利用 vislets 和 LSTM 预测未来的行人位置和头部姿势，相比之前的方法效果有了显著提升，在 Zara01、Zara02、UCY 和 TownCentre 四个数据集上均取得了最佳表现。