论文标题
量化长期人类轨迹预测的标准基准数据集的复杂性
Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction
论文作者
论文摘要
量化轨迹数据集复杂性的方法在基准人类轨迹预测模型中仍然是缺少的部分。为了更好地了解轨迹预测任务的复杂性并遵循直觉，更复杂的数据集包含更多信息，提出了一种量化基于原型数据集表示数据集中包含的信息量的方法。数据集表示是通过首先采用非平凡的空间序列比对来获得的，该序列对准可以实现随后的学习矢量量化（LVQ）阶段。对几个人类轨迹预测基准数据集进行了大规模的复杂性分析，然后简要讨论了人类轨迹预测和基准测试的指示。
Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation is proposed. The dataset representation is obtained by first employing a non-trivial spatial sequence alignment, which enables a subsequent learning vector quantization (LVQ) stage. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets, followed by a brief discussion on indications for human trajectory prediction and benchmarking.
