DOI: 10.5281/zenodo.15313857
title: OV-SKTGCNN datasets(ETH UVY)

description: Dataset Repository for: Enhanced Pedestrian Trajectory Prediction via Overlapping Field-of-View Domains and Integrated Kolmogorov-Arnold Networks (OV-SKTGCNN)



This repository serves as a documentation hub for the trajectory datasets used in the OV-SKTGCNN model. Note: This repository does NOT contain any code or model implementations - it solely provides dataset provenance information.

Dataset Provenance

The trajectory data used in our study follows the exact implementation from Social-STGCNN [17], processed from two public pedestrian video sources:

Original Video Sources





ETH Pedestrian Dataset Original videos published by Pellegrini et al. [27] Access raw videos at: https://data.vision.ee.ethz.ch/cvl/aem/ewap_dataset_full.tgz




UCY Crowd Dataset Original videos published by Lerner et al. [28] Access raw videos at: https://graphics.cs.ucy.ac.cy/research/downloads/crowd-data



Processed Trajectory Data

The structured trajectory files containing:





Agent positions in world coordinates




Velocity measurements




Timestamp information



Are available through the Social-STGCNN GitHub repository: https://github.com/abduallahmohamed/Social-STGCNN

The data schema and preprocessing methodology are fully described in the original Social-STGCNN paper [17].

Data Compatibility Verification

Our OV-SKTGCNN implementation maintains strict compatibility with the original data format:





No modifications to data structures




Preserved original sampling rates (2.5 Hz)




Consistent coordinate normalization




Identical train/test splits



This ensures direct comparability with baseline models using the same dataset configuration.

Usage Notes

This repository is provided for:





Transparent documentation of data provenance




Easy reference for replication studies




Comparison with our OV-SKTGCNN results



All dataset copyright remains with the original authors. Please observe the license terms from both the original video sources and the Social-STGCNN repository when using this data.

Citation

When using this data configuration, please cite both the original datasets and the Social-STGCNN processing methodology:

@INPROCEEDINGS{5459260,  author={Pellegrini, S. and Ess, A. and Schindler, K. and van Gool, L.},  booktitle={2009 IEEE 12th International Conference on Computer Vision},   title={You'll never walk alone: Modeling social behavior for multi-target tracking},   year={2009},  volume={},  number={},  pages={261-268},  keywords={Predictive models;Vehicle dynamics;Layout;Humans;Computer vision;Trajectory;Cameras;Legged locomotion;Path planning;Computer science},  doi={10.1109/ICCV.2009.5459260}}@inproceedings{lerner2007crowds,  title={Crowds by example},  author={Lerner, Alon and Chrysanthou, Yiorgos and Lischinski, Dani},  booktitle={Computer graphics forum},  volume={26},  number={3},  pages={655--664},  year={2007},  organization={Wiley Online Library}}@article{almomen2020social,  title={Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction},  author={Almomen, Abduallah and Kim, Kyungtae and How, Jonathan},  journal={arXiv preprint arXiv:2002.11927},  year={2020}}