@article{ref1,
title="The autonomous train vision: embedded AI for pedestrians monitoring",
journal="Transportation research procedia",
year="2023",
author="Mahtani, Ankur and Doba, Eddy and Ammad, Nadia",
volume="72",
number="",
pages="949-956",
abstract="When you develop a perception system for rolling stocks of the railway sector, you are dealing with huge infrastructures and harsh conditions never encountered. From the integration studies to the final software implementation of the computer vision algorithm in the embedded system, our work puts on paper these steps and challenges encountered by focusing on one specific use-case of the environment monitoring of a train: pedestrians tracking in front of the train. Pedestrian monitoring is a key aspect of autonomous train operation as it involves ensuring the safety of people in the vicinity of the tracks. This scenario covers most of the requirements for automatic detection scenarios at the front of a train: close and distant moving object detection, robust object tracking, person state analysis with high confidence rates to ensure safety for human life and for train driving.<p /> <p>Language: en</p>",
language="en",
issn="2352-1465",
doi="10.1016/j.trpro.2023.11.522",
url="http://dx.doi.org/10.1016/j.trpro.2023.11.522"
}