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 - The Transactions of the Korea Information Processing Society 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.