Name
Title | ||
|---|---|---|
Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus. |
Abstract | ||
|---|---|---|
Predicting the collective motion of a group of pedestrians (a crowd) under the vehicle influence is essential for the development of autonomous vehicles to deal with mixed urban scenarios where interpersonal interaction and vehicle-crowd interaction (VCI) are significant. This usually requires a model that can describe individual pedestrian motion under the influence of nearby pedestrians and the vehicle. This study proposed two pedestrian trajectory dataset, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration. The trajectories of pedestrians, as well as vehicles, were extracted by processing video frames that come from a down-facing camera mounted on a hovering drone as the recording equipment. The final trajectories were refined by a Kalman Filter, in which the pedestrian velocity was also estimated. The statistics of the velocity magnitude distribution demonstrated the validity of the proposed dataset. In total, there are approximate 340 pedestrian trajectories in CITR dataset and 1793 pedestrian trajectories in DUT dataset. The dataset is available at GitHub. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IVS.2019.8814092 | IEEE Intelligent Vehicles Symposium |
Field | DocType | Volume |
Computer vision,Magnitude (mathematics),Pedestrian,Collective motion,Nonlinear system,General purpose,Computer science,Kalman filter,Artificial intelligence,Drone,Trajectory,Machine learning | Journal | abs/1902.00487 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongfang Yang | 1 | 0 | 0.34 |
| Linhui Li | 2 | 0 | 0.34 |
| Keith Redmill | 3 | 101 | 13.99 |
| Ümit Özgüner | 4 | 0 | 0.34 |