Kai Chen

Northeast Electric Power University, Nanjing University of Aeronautics and Astronautics

Papers

Total Citations

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About

Kai Chen is a computer vision researcher whose work sits at the intersection of intelligent transportation systems, human behavior analysis, and deep learning. His research focuses on object detection and trajectory prediction, with particular emphasis on making autonomous and surveillance systems more perceptive and reliable in real-world environments. Chen's most influential contribution, "Vehicle and Pedestrian Detection Using Support Vector Machine and Histogram of Oriented Gradients Features" (2013), has garnered 30 citations and remains a foundational reference in the field. This work advanced classical machine learning approaches to detection tasks critical for robotics, automotive safety, and surveillance — domains where accurate scene understanding is paramount. More recently, Chen has embraced the deep learning era with his 2023 paper on a Convolutional Transformer Network for predicting future pedestrian locations in first-person videos, leveraging depth maps and 3D pose estimation. This forward-looking research addresses the complex challenge of anticipatory perception in egocentric systems — a growing priority for wearable technology and autonomous vehicles alike. Spanning a decade of contributions, Chen's trajectory reflects a researcher who has evolved alongside the field, bridging classical computer vision fundamentals with cutting-edge neural architectures to address real-world safety and perception challenges.