MONCE Tracking Metrics: a comprehensive quantitative performance evaluation methodology for object tracking
This work addresses the problem of comprehensive performance evaluation for tracking models in defense applications, but it is incremental as it expands on existing benchmarks.
The paper tackles the challenge of evaluating tracking models, especially for non-contiguous, multi-object trackers in defense applications, by proposing a suite of MONCE metrics that provide performance benchmarks and diagnostic insights.
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.