TY  - JOUR
T1  - A Study of Tram-Pedestrian Collision Prediction Method
Using YOLOv5 and Motion Vector
AU  - Kim, Young-Min 
AU  - An, Hyeon-Uk 
AU  - Jeon, Hee-gyun 
AU  - Kim, Jin-Pyeong 
AU  - Jang, Gyu-Jin 
AU  - Hwang, Hyeon-Chyeol 
JO  - KIPS Transactions on Software and Data Engineering
PY  - 2021
DA  - 2021/1/30
DO  - https://doi.org/10.3745/KTSDE.2021.10.12.561
KW  - Tram
KW  - Dense Optical Flow
KW  - Estimation of Collision point
KW  - TTC(Time-To-Collision)
KW  - YOLOv5
AB  - 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.