Pedestrian Intention Estimation
PIE is a new dataset for studying pedestrian behavior in traffic. PIE contains over 6 hours of footage recorded in typical traffic scenes with on-board camera. It also provides accurate vehicle information from OBD sensor (vehicle speed, heading direction and GPS coordinates) synchronized with video footage.
Rich spatial and behavioral annotations are available for pedestrians and vehicles that potentially interact with the ego-vehicle as well as for the relevant elements of infrastructure (traffic lights, signs and zebra crossings).
There are over 300K labeled video frames with 1842 pedestrian samples making this the largest publicly available dataset for studying pedestrian behavior in traffic.
Variants: PIE
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Trajectory Prediction | SGNet | Stepwise Goal-Driven Networks for Trajectory … | 2021-03-25 |
| Trajectory Prediction | Bitrap-D | BiTraP: Bi-directional Pedestrian Trajectory Prediction … | 2020-07-29 |
| Trajectory Prediction | FOL-X | Unsupervised Traffic Accident Detection in … | 2019-03-02 |
| Trajectory Prediction | Bayesian-LSTM | Long-Term On-Board Prediction of People … | 2017-11-24 |
Recent papers with results on this dataset: