用量子启发算法提升车载多目标跟踪系统在长时间遮挡下的追踪能力
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines
- 采用模拟分裂算法的可嵌入伊辛机解决复杂关联问题
- 实测系统达每秒23帧，支持长期遮挡场景下稳定追踪
- 适合自动驾驶车辆实时感知系统研发者参考
自主移动车辆所需的目标多对象追踪功能包含目标检测与时空关联。尽管近年来机器学习在构建已识别目标与当前视频帧中检测到目标之间的相似性矩阵方面取得显著进展，但最终决定时空关联的分配问题——一个组合优化难题——仍缺乏有效解决方案。本文提出一种车载多目标追踪系统，具备处理长期遮挡事件的灵活分配能力。通过将该分配问题建模为非确定性多项式时间难解问题，并基于量子启发算法模拟分裂（simulated bifurcation）实现求解，结合车载计算平台，实现了平均23帧/秒的系统级吞吐量，验证了实时性与增强功能的有效性。
原文摘要 · Abstract (English)
A cognitive function of tracking multiple objects, needed in autonomous mobile vehicles, comprises object detection and their temporal association. While great progress owing to machine learning has been recently seen for elaborating the similarity matrix between the objects that have been recognized and the objects detected in a current video frame, less for the assignment problem that finally determines the temporal association, which is a combinatorial optimization problem. Here we show an in-vehicle multiple object tracking system with a flexible assignment function for tracking through multiple long-term occlusion events. To solve the flexible assignment problem formulated as a nondeterministic polynomial time-hard problem, the system relies on an embeddable Ising machine based on a quantum-inspired algorithm called simulated bifurcation. Using a vehicle-mountable computing platform, we demonstrate a realtime system-wide throughput (23 frames per second on average) with the enhanced functionality.
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