Sei-Wang Chen

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About

Sei-Wang Chen is a computer vision researcher whose work centers on human action recognition, deep learning, and intelligent robotic systems. Chen's research has made meaningful contributions to the development of vision-based frameworks that enable robots to perceive and respond to human behavior in real-world environments. A consistent thread throughout their work is the challenge of building robust recognition systems capable of functioning under dynamic conditions, such as moving cameras and varying viewpoints — problems that are critical for practical deployment in companion and mobile robotics. Chen's most-cited work, "A Vision-Based Human Action Recognition System for Companion Robots and Human Interaction" (2018, 13 citations), introduced a pipeline integrating motion map construction, feature extraction, and action classification using Kinect depth and color imaging. Subsequent research expanded this foundation into deep learning territory, producing systems that maintain accuracy even as robot cameras navigate toward targets from multiple angles. By 2021, Chen's lab had developed fully online recognition systems capable of real-time deployment in indoor smart mobile robot settings. Collectively, Chen's publications reflect a sustained commitment to bridging computer vision and human-robot interaction, offering researchers and engineers practical tools for building socially aware robotic companions.