Hiroaki Minoura

Papers

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About

Hiroaki Minoura is a computer vision researcher whose work focuses on forecasting human activities from video data, with a particular emphasis on crowd dynamics. His primary research areas include visual forecasting, crowd analysis, and mobile robotics applications. Minoura’s major contribution is the introduction of a novel task called **crowd density forecasting**, which predicts future crowd distributions in video scenes by modeling patch-based dynamics. This work addresses a long-standing challenge in computer vision and robotics, enabling applications such as mobile robot navigation, autonomous driving, and drone landing. His most-cited paper, "Crowd Density Forecasting by Modeling Patch-Based Dynamics" (2020), has garnered 14 citations, demonstrating growing interest in this emerging area. By shifting focus from individual tracking to holistic density prediction, Minoura’s approach offers a scalable solution for real-time crowd monitoring and proactive decision-making in dynamic environments. His research bridges the gap between visual perception and predictive modeling, providing a foundation for safer and more efficient autonomous systems in crowded spaces.