@inproceedings{2ba38ad1ffcd476fa711c4ea630591d9,
title = "Pedestrian tracking and stereo matching of tracklets for autonomous vehicles",
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.",
keywords = "Autonomous vehicles, Pedestrian tracking, Stereo matching, Tracklet clustering, Trajectory reconstruction",
author = "Hao Xue and Huynh, {Du Q.} and Mark Reynolds",
year = "2019",
doi = "10.1109/VTCSpring.2019.8746329",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
booktitle = "2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings",
address = "United States",
note = "89th IEEE Vehicular Technology Conference, VTC Spring 2019 ; Conference date: 28-04-2019 Through 01-05-2019",
}