论文标题
OPENTRAJ：评估人类轨迹数据集中的预测复杂性
OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets
论文作者
论文摘要
在过去的几年中，人类轨迹预测（HTP）已经获得了很多动力，并提出了许多解决方案来解决它。适当的基准测试是比较方法的关键问题，本文解决了评估给定数据集在预测问题方面的复杂程度的问题。为了评估数据集复杂性，我们定义了三个概念的一系列指标：轨迹可预测性；轨迹规律性；上下文复杂性。根据这些指标，我们比较了HTP中使用的最常见数据集，并讨论这可能意味着HTP算法的基准测试。我们的源代码在GitHub上发布。
Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. Proper benchmarking being a key issue for comparing methods, this paper addresses the question of evaluating how complex is a given dataset with respect to the prediction problem. For assessing a dataset complexity, we define a series of indicators around three concepts: Trajectory predictability; Trajectory regularity; Context complexity. We compare the most common datasets used in HTP in the light of these indicators and discuss what this may imply on benchmarking of HTP algorithms. Our source code is released on Github.
