Multi Camera Multi

Multi-camera multi-object tracking (MC-MOT) aims to accurately track multiple objects across multiple camera views, overcoming challenges like occlusions and inconsistent perspectives. Recent research heavily utilizes graph-based methods, often incorporating spatial and temporal information through sophisticated graph structures and algorithms like lifted multicut, to achieve robust global associations between objects across cameras. This focus on improved data association and the development of more efficient algorithms, such as single-stage global association approaches, is driven by the increasing demand for reliable tracking in applications like autonomous driving and city-scale surveillance. The resulting advancements significantly improve tracking accuracy and efficiency compared to single-camera approaches.