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Pedestrian Intention Estimation (PIE)

This repository contains code and annotations for the Pedestrian Intention Estimation (PIE) dataset: A. Rasouli, I. Kotseruba, T. Kunic, J.K. Tsotsos, PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation, ICCV, 2019 and a series of scripts for visualization and scenario evaluation.

Download videos

Videos are grouped into 6 sets corresponding to different routes driven in Toronto, Canada.

The total size of all video clips is approx. 74GB.

Download links YorkU server Google Drive

Content

• annotations: Contains the annotations and a script for extracting images from raw videos
• scenarioEval: Contains code for scenario generation and metrics for trajectory and action prediction
• visualization: Contains a series of scripts for visualizing data samples and illustrating trajectory and action prediction modules
• model_outputs: Contains models' outputs for behavior prediction to be used for evaluation and visualization
• utilities: Contains configuration file for evaluation and visualization, dataset interfaces, and other utility functions
• camera_params: Contains camera parameters for the PIE dataset

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Citation

If you use our dataset, please cite:

@InProceedings{Rasouli_2019_ICCV,
author = {Rasouli, Amir and Kotseruba, Iuliia and Kunic, Toni and Tsotsos, John K.},
title = {PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2019}}

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Authors

• Amir Rasouli
• Iuliia Kotseruba

Please send an email to yulia_k@eecs.yorku.ca or arasouli.ai@gmail.com if there are any problems with downloading or using the data.

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License

This project is licensed under the MIT License - see the LICENSE file for details