Hyunseok Choi

Since January 2023, I’ve joined Tmax AI as an AI engineer to develop Generative AI models, enabling users to be more productive and accessible.

My research interests are Generative AI, Computer Vision, Neural Network, and Unreal Engine. My ongoing project is focused on productivity improvment of Unreal Engine using Generative AI models.

I completed a M.S. student in the Department of Electrical and Electronic Engineering at Yonsei University in 2023, where I was advised by Prof. Daeeun Kim.

Prior to M.S. course, I received my B.S. degree in the Department of Human Environment and Design, the Cognitive Science, and the Applied Statistics at Yonsei University in 2021.

news

Jun 29, 2022	Best oral award at The International Council on Electrical Engineering Conference 2022 (ICEE 2022).
Feb 28, 2021	Enter the M.S. course at Yonsei University.
Jul 22, 2020	Internship at the Biological Cybernetics laboratory.
Feb 28, 2015	Enter the B.S. course at Yonsei University.

selected publications

Sensors

Moving object tracking based on sparse optical flow with moving window and target estimator

Hosik Choi, Byungmun Kang, and DaeEun Kim

Sensors, 2022

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Moving object detection and tracking are technologies applied to wide research fields including traffic monitoring and recognition of workers in surrounding heavy equipment environments. However, the conventional moving object detection methods have faced many problems such as much computing time, image noises, and disappearance of targets due to obstacles. In this paper, we introduce a new moving object detection and tracking algorithm based on the sparse optical flow for reducing computing time, removing noises and estimating the target efficiently. The developed algorithm maintains a variety of corner features with refreshed corner features, and the moving window detector is proposed to determine the feature points for tracking, based on the location history of the points. The performance of detecting moving objects is greatly improved through the moving window detector and the continuous target estimation. The memory-based estimator provides the capability to recall the location of corner features for a period of time, and it has an effect of tracking targets obscured by obstacles. The suggested approach was applied to real environments including various illumination (indoor and outdoor) conditions, a number of moving objects and obstacles, and the performance was evaluated on an embedded board (Raspberry pi4). The experimental results show that the proposed method maintains a high FPS (frame per seconds) and improves the accuracy performance, compared with the conventional optical flow methods and vision approaches such as Haar-like and Hog methods.