PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction

By Amir Rasouli, Iuliia Kotseruba, Toni Kunic, John K. Tsotsos. IEEE International Conference on Computer Vision, 2019

434 citations recorded by the source corpus.

Pedestrian behavior anticipation is a key challenge in the design of assistive and autonomous driving systems suitable for urban environments. An intelligent system should be able to understand the intentions or underlying motives of pedestrians and to predict their forthcoming actions. To date, only a few public datasets were proposed for the purpose of studying pedestrian behavior prediction in the context of intelligent driving. To this end, we propose a novel large-scale dataset designed for pedestrian intention estimation (PIE). We conducted a large-scale human experiment to establish human reference data for pedestrian intention in traffic scenes. We propose models for estimating pedestrian crossing intention and predicting their future trajectory. Our intention estimation model achieves 79% accuracy and our trajectory prediction algorithm outperforms state-of-the-art by 26% on the proposed dataset. We further show that combining pedestrian intention with observed motion improves trajectory prediction. The dataset and models are available at http://data.nvision2.eecs.yorku.ca/PIE_dataset/.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/36194c76ce53be8e8fba71acbf8d235c7b39342b

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In Automotive Engineering

In Computer Vision and Pattern Recognition

In Safety, Risk, Reliability and Quality

Dacheng Tao

Citations in the corpus

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “car [261], estimating the intention of cars and pedestrians and predicting their trajectories [244], [245].”