Kyung Hee University · 情報科学
Professor Young-Koo Lee's research lab specializes in intelligent video analytics and human activity understanding, focusing on leveraging sensor and video data for healthcare, smart environments, and surveillance applications. The lab develops advanced machine learning and computer vision techniques for long-term activity monitoring, behavior analysis, and action prediction in real-world settings such as smart homes and public spaces. Key research directions include personalized health monitoring, lifestyle disease management, and scalable video analytics for large-scale surveillance systems. The lab emphasizes the integration of deep learning with real-time data processing to enable proactive healthcare and intelligent environmental systems.
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Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition
In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towa
Activity monitoring of a person for a long-term would be helpful for controlling lifestyle associated diseases. Such diseases are often linked with the way a person lives. An unhealthy and irregular standard of living influences the risk of such diseases in the later part of one's life. The symptoms and the initial signs of these diseases are common to the people with irregular lifestyle. In this paper, we propose a novel healthcare framework to manage lifestyle diseases using long-term activity
In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchabl
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