论文标题
MONCE跟踪指标：对象跟踪的全面定量性能评估方法
MONCE Tracking Metrics: a comprehensive quantitative performance evaluation methodology for object tracking
论文作者
论文摘要
评估跟踪模型性能是一项复杂的任务，尤其是对于在国防应用中至关重要的非连接的多对象跟踪器中。尽管有各种出色的跟踪基准可用，但这项工作却将其扩展，以量化长期，不连续，多对象和检测模型的辅助跟踪器的性能。我们提出了一套Monce（多目标非连续实体）图像跟踪指标，该指标既可以提供客观跟踪模型性能基准，又提供了以预期的平均重叠，短/长期重新确定，跟踪召回，追踪召回，寿命精确，寿命，寿命，定位，本地化，本地化，预测和缺席的预期重叠形式来推动跟踪模型开发的诊断见解。
Evaluating tracking model performance is a complicated task, particularly for non-contiguous, multi-object trackers that are crucial in defense applications. While there are various excellent tracking benchmarks available, this work expands them to quantify the performance of long-term, non-contiguous, multi-object and detection model assisted trackers. We propose a suite of MONCE (Multi-Object Non-Contiguous Entities) image tracking metrics that provide both objective tracking model performance benchmarks as well as diagnostic insight for driving tracking model development in the form of Expected Average Overlap, Short/Long Term Re-Identification, Tracking Recall, Tracking Precision, Longevity, Localization and Absence Prediction.
