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About

Haibin Ling is a leading researcher in computer vision and robotics, with a primary focus on planar object tracking and its real-world applications. His major contributions include the creation of the first comprehensive benchmark for planar object tracking in unconstrained environments, notably the "Planar Object Tracking in the Wild" dataset (2018, 48 citations), which has become a standard for evaluating algorithms outside laboratory settings. Ling has also advanced direct visual tracking methods, developing illumination-insensitive techniques using efficient second-order minimization (ESM) to improve robustness in robotic vision. His work extends to multi-agent systems, where he integrates computer vision with pursuit-evasion game theory for ground robots (2019, 11 citations), and to bionic drone-based tracking, exemplified by the BioDrone benchmark (2023, 13 citations). Ling’s research has achieved significant impact, with his top-cited papers accumulating over 120 citations, and his innovations have been applied in diverse areas, including low-frame-rate video tracking and medical robotics, such as an acupuncture robot system (2025). His benchmarks and algorithms continue to shape the field, providing essential tools for both academic research and practical robotic applications.