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.

这篇研究论文介绍了一种利用量子退火来加快计算速度、通过集成物体跟踪过程提高跟踪准确性的新方法，并提出了一种改进匹配集成过程的方法。利用 MOT 的序列性质，通过逆向退火进一步增强了跟踪方法。实验证实，每个跟踪过程仅需要 3 微秒的退火时间即可保持高准确性。这种方法对实时 MOT 应用具有重要潜力，包括城市交通灯控制的交通流量测量、自主机器人和车辆的碰撞预测以及工厂中大规模生产产品的管理。