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.
翻译: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,建议了从原型数据集代表中量化数据集所含信息数量的方法。数据集的表示方式首先采用非三轨空间序列对齐,从而能够随后进入学习矢量量化阶段。对几个人类轨迹预测基准数据集进行了大规模复杂分析,随后就人类轨迹预测和基准指标进行了简短讨论。