@article{MC1E08E5A,
title = "A Study of Tram-Pedestrian Collision Prediction Method
Using YOLOv5 and Motion Vector",
journal = "The Transactions of the Korea Information Processing Society",
year = "2021",
issn = "null",
doi = "https://doi.org/10.3745/KTSDE.2021.10.12.561",
author = "Young-Min Kim/Hyeon-Uk An/Hee-gyun Jeon/Jin-Pyeong Kim/Gyu-Jin Jang/Hyeon-Chyeol Hwang",
keywords = "Tram, Dense Optical Flow, Estimation of Collision point, TTC(Time-To-Collision), YOLOv5",
abstract = "In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields
of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real
time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to
detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed
using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local
dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a
system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents."
}