When humans navigate a crowed space such as a university campus or the
sidewalks of a busy street, they follow common sense rules based on social
etiquette. In this paper, we argue that in order to enable the design of new
algorithms that can take fully advantage of these rules to better solve tasks
such as target tracking or trajectory forecasting, we need to have access to
better data in the first place. To that end, we contribute the very first large
scale dataset (to the best of our knowledge) that collects images and videos of
various types of targets (not just pedestrians, but also bikers, skateboarders,
cars, buses, golf carts) that navigate in a real-world outdoor environment such
as a university campus. We present an extensive evaluation where different
methods for trajectory forecasting are evaluated and compared. Moreover, we
present a new algorithm for trajectory prediction that exploits the complexity
of our new dataset and allows to: i) incorporate inter-class interactions into
trajectory prediction models (e.g, pedestrian vs bike) as opposed to just
intra-class interactions (e.g., pedestrian vs pedestrian); ii) model the degree
to which the social forces are regulating an interaction. We call the latter
"social sensitivity"and it captures the sensitivity to which a target is
responding to a certain interaction. An extensive experimental evaluation
demonstrates the effectiveness of our novel approach.

本文提出了一个集成图像和视频的大规模数据集， 收集了各种类型的目标（不仅是行人，还有自行车，滑板车，汽车，公交车，高尔夫球车）在现实世界室外环境中如何运动的数据。本文还提出了新的轨迹预测算法，并且这个算法利用了新数据集的复杂度，可以模拟交互中的社会力量对行人的影响，有效地解决了目标跟踪与轨迹预测问题。