Global Data Association for Multiple Pedestrian Tracking

Global Data Association for Multiple Pedestrian Tracking

Explore a doctoral dissertation defense on advanced multi-target tracking techniques for pedestrians. Delve into novel data association methods using Generalized Maximum Clique Problem (GMCP) and Generalized Maximum Multi Clique Problem (GMMCP) formulations. Learn about global optimization approaches incorporating motion and appearance, and discover solutions for tracking in extremely crowded scenes using Binary Quadratic Programming. Examine the limitations of current methods, proposed improvements, and experimental results across various datasets. Gain insights into cutting-edge algorithms for efficient tracking of hundreds of targets in complex scenarios like marathons and political rallies.