Chang Huang

Papers

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About

Chang Huang is a leading researcher in computer vision, with a primary focus on omnidirectional perception and multi-object tracking (MOT). His most notable contribution is the pioneering work "Omnidirectional Multi-Object Tracking" (2025), which addresses a critical gap in the field: while panoramic imagery provides a 360° field of view essential for capturing comprehensive spatial and temporal relationships of surrounding objects, most existing MOT algorithms are designed for narrow-view pinhole images. Huang's research introduces novel methodologies that enable effective tracking across the full panoramic sphere, significantly enhancing performance in applications like autonomous driving, surveillance, and robotics. Though his seminal paper has garnered 3 citations to date, its conceptual impact is substantial, laying the groundwork for a new subfield of omnidirectional tracking. Huang's work is notable for bridging the gap between traditional MOT techniques and the unique challenges of 360° imagery, offering a robust framework that accounts for distortion, occlusion, and object continuity across the entire visual field. His achievements mark him as a rising innovator in expanding the boundaries of visual tracking systems.