Abstract
The prediction of the surrounding pedestrians' walking paths is a vital part for autonomous driving systems in the aspect of traffic safety. In this paper, we propose a pipeline which tracks pedestrians captured by a stereo camera system onboard a mobile vehicle, composes the pedestrian tracklets, clusters the tracklets to form trajectories, and matches the trajectories. The output 3D pedestrian trajectories can be used for further applications such as pedestrian trajectory prediction for driverless vehicles. Our algorithm has been compared with various state-of-art pedestrian tracking methods. Our experimental results show that the visual temporal features computed by our algorithm are effective for trajectory representation and that, by incorporating tracklet clustering into the pipeline, the pedestrian tracking performance is improved.
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| ISBN (Electronic) | 9781728112176 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | 89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia Duration: 28 Apr 2019 → 1 May 2019 |
Publication series
| Name | IEEE Vehicular Technology Conference |
|---|---|
| Volume | 2019-April |
| ISSN (Print) | 1550-2252 |
Conference
| Conference | 89th IEEE Vehicular Technology Conference, VTC Spring 2019 |
|---|---|
| Country | Malaysia |
| City | Kuala Lumpur |
| Period | 28/04/19 → 1/05/19 |
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Pedestrian tracking and stereo matching of tracklets for autonomous vehicles. / Xue, Hao; Huynh, Du Q.; Reynolds, Mark.
2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings. IEEE, Institute of Electrical and Electronics Engineers, 2019. 8746329 (IEEE Vehicular Technology Conference; Vol. 2019-April).Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - Pedestrian tracking and stereo matching of tracklets for autonomous vehicles
AU - Xue, Hao
AU - Huynh, Du Q.
AU - Reynolds, Mark
PY - 2019
Y1 - 2019
N2 - The prediction of the surrounding pedestrians' walking paths is a vital part for autonomous driving systems in the aspect of traffic safety. In this paper, we propose a pipeline which tracks pedestrians captured by a stereo camera system onboard a mobile vehicle, composes the pedestrian tracklets, clusters the tracklets to form trajectories, and matches the trajectories. The output 3D pedestrian trajectories can be used for further applications such as pedestrian trajectory prediction for driverless vehicles. Our algorithm has been compared with various state-of-art pedestrian tracking methods. Our experimental results show that the visual temporal features computed by our algorithm are effective for trajectory representation and that, by incorporating tracklet clustering into the pipeline, the pedestrian tracking performance is improved.
AB - The prediction of the surrounding pedestrians' walking paths is a vital part for autonomous driving systems in the aspect of traffic safety. In this paper, we propose a pipeline which tracks pedestrians captured by a stereo camera system onboard a mobile vehicle, composes the pedestrian tracklets, clusters the tracklets to form trajectories, and matches the trajectories. The output 3D pedestrian trajectories can be used for further applications such as pedestrian trajectory prediction for driverless vehicles. Our algorithm has been compared with various state-of-art pedestrian tracking methods. Our experimental results show that the visual temporal features computed by our algorithm are effective for trajectory representation and that, by incorporating tracklet clustering into the pipeline, the pedestrian tracking performance is improved.
KW - Autonomous vehicles
KW - Pedestrian tracking
KW - Stereo matching
KW - Tracklet clustering
KW - Trajectory reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85068974362&partnerID=8YFLogxK
U2 - 10.1109/VTCSpring.2019.8746329
DO - 10.1109/VTCSpring.2019.8746329
M3 - Conference paper
T3 - IEEE Vehicular Technology Conference
BT - 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
PB - IEEE, Institute of Electrical and Electronics Engineers
ER -