Minglang Lu
Papers
2
Total Citations
30
H-Index
2
About
Minglang Lu is a researcher advancing the frontiers of computer vision, with a focus on human–object interaction (HOI) detection and multi-object tracking (MOT). His work addresses critical challenges in understanding complex visual scenes, particularly how machines can simultaneously recognize human actions and track multiple moving objects. Lu’s most-cited paper, “Intra- and inter-instance Location Correlation Network for human–object interaction detection” (2025, 19 citations), introduces a novel framework that leverages spatial correlations within and between instances to improve HOI detection accuracy—a key step for applications in robotics and surveillance. His second highly cited work, “Multi-object tracking using score-driven hierarchical association strategy between predicted tracklets and objects” (2024, 11 citations), proposes an efficient hierarchical association method that enhances tracking robustness in crowded or occluded environments. With a growing citation impact, Lu’s contributions are shaping the next generation of intelligent vision systems, offering practical solutions for real-time scene understanding. His research not only pushes theoretical boundaries but also provides scalable algorithms for autonomous systems, making him a promising voice in the field.
Research Focus
Key Achievements
Top Papers
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- 2