论文标题
多对象跟踪的有效数据关联和不确定性量化
Efficient Data Association and Uncertainty Quantification for Multi-Object Tracking
论文作者
论文摘要
强大的数据关联对于分析复杂场景中的长期运动轨迹至关重要。在没有运动型歧义时期，轨迹精度会受到损害，从而降低了后续分析的质量。基于常见的优化方法通常会忽略这些事件引起的不确定性量化。因此，我们提出了联合后验跟踪器（JPT），这是一种贝叶斯多对象跟踪算法，可以鲁ship，在关联和轨迹的后部。新颖的基于置换的建议是为探索与合理关联假设相对应的后验模式而设计的。与现有基线相比，JPT表现出具有更准确的数据关联的不确定性表示，标准指标上具有出色的性能。我们还展示了JPT应用于用户在循环注释的自动调度方面的实用程序，以提高轨迹质量。
Robust data association is critical for analysis of long-term motion trajectories in complex scenes. In its absence, trajectory precision suffers due to periods of kinematic ambiguity degrading the quality of follow-on analysis. Common optimization-based approaches often neglect uncertainty quantification arising from these events. Consequently, we propose the Joint Posterior Tracker (JPT), a Bayesian multi-object tracking algorithm that robustly reasons over the posterior of associations and trajectories. Novel, permutation-based proposals are crafted for exploration of posterior modes that correspond to plausible association hypotheses. JPT exhibits more accurate uncertainty representation of data associations with superior performance on standard metrics when compared to existing baselines. We also show the utility of JPT applied to automatic scheduling of user-in-the-loop annotations for improved trajectory quality.
