MONCE tracking metrics: a comprehensive quantitative performance evaluation methodology for object tracking

Kenneth Rapko; Wanlin Xie; Andrew Walsh

doi:10.1117/12.2618631

31 May 2022 MONCE tracking metrics: a comprehensive quantitative performance evaluation methodology for object tracking

Kenneth Rapko, Wanlin Xie, Andrew Walsh

Proceedings Volume 12096, Automatic Target Recognition XXXII; 1209605 (2022) https://doi.org/10.1117/12.2618631
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Kenneth Rapko, Wanlin Xie, and Andrew Walsh "MONCE tracking metrics: a comprehensive quantitative performance evaluation methodology for object tracking", Proc. SPIE 12096, Automatic Target Recognition XXXII, 1209605 (31 May 2022); https://doi.org/10.1117/12.2618631

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