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MONCE Tracking Metrics: a comprehensive quantitative performance evaluation methodology for object tracking

2022-04-11 17:32:03
Kenneth Rapko, Wanlin Xie, Andrew Walsh
arXiv_CV
               
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.

Abstract (translated)

URL

https://arxiv.org/abs/2204.05280

PDF

https://arxiv.org/pdf/2204.05280.pdf

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