Enhancing Multiple Object Tracking Accuracy via Quantum Annealing
This could enable real-time applications like traffic control and autonomous vehicles, though it appears incremental as it builds on existing quantum annealing methods.
This study tackled the challenge of balancing speed and accuracy in multiple object tracking by introducing a quantum annealing-based approach that maintains high accuracy with an annealing time of just 3 μs per tracking process.
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 $μ$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.