  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.
