  Multiple object tracking (MOT), a key task in image recognition, presents a
persistent challenge in balancing processing speed and tracking accuracy. This
study introduces a novel approach that leverages quantum annealing (QA) to
expedite computation speed, while enhancing tracking accuracy through the
ensembling of object tracking processes. A method to improve the matching
integration process is also proposed. By utilizing the sequential nature of
MOT, this study further augments the tracking method via reverse annealing
(RA). Experimental validation confirms the maintenance of high accuracy with an
annealing time of a mere 3 $\mu$s per tracking process. The proposed method
holds significant potential for real-time MOT applications, including traffic
flow measurement for urban traffic light control, collision prediction for
autonomous robots and vehicles, and management of products mass-produced in
factories.
