# Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction

arXiv: https://arxiv.org/abs/2005.13934 · HTML: https://mlsanity.com/paper/2005.13934/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 5 of 5)

- [GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model](https://mlsanity.com/paper/2209.07857/) (2022)
- [Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction](https://mlsanity.com/paper/2107.00422/) (2021)
- [MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction](https://mlsanity.com/paper/2106.16009/) (2021)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [ViFiT: Reconstructing Vision Trajectories from IMU and Wi-Fi Fine Time Measurements](https://mlsanity.com/paper/2310.03140/) (2023)

## Abstract (arXiv)

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.