A Study of Tram-Pedestrian Collision Prediction Method Using YOLOv5 and Motion Vector


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 12, pp. 561-568, Dec. 2021
https://doi.org/10.3745/KTSDE.2021.10.12.561,   PDF Download:  
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.


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Cite this article
[IEEE Style]
Y. Kim, H. An, H. Jeon, J. Kim, G. Jang, H. Hwang, "A Study of Tram-Pedestrian Collision Prediction Method Using YOLOv5 and Motion Vector," KIPS Transactions on Software and Data Engineering, vol. 10, no. 12, pp. 561-568, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.12.561.

[ACM Style]
Young-Min Kim, Hyeon-Uk An, Hee-gyun Jeon, Jin-Pyeong Kim, Gyu-Jin Jang, and Hyeon-Chyeol Hwang. 2021. A Study of Tram-Pedestrian Collision Prediction Method Using YOLOv5 and Motion Vector. KIPS Transactions on Software and Data Engineering, 10, 12, (2021), 561-568. DOI: https://doi.org/10.3745/KTSDE.2021.10.12.561.