# Visual object tracking performance measures revisited

arXiv: https://arxiv.org/abs/1502.05803 · HTML: https://mlsanity.com/paper/1502.05803/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2015.

## Built on by (most cited first, 13 of 13)

- [Perception Test: A Diagnostic Benchmark for Multimodal Video Models](https://mlsanity.com/paper/2305.13786/) (2023)
- [Separable Self and Mixed Attention Transformers for Efficient Object Tracking](https://mlsanity.com/paper/2309.03979/) (2023)
- [Is First Person Vision Challenging for Object Tracking?](https://mlsanity.com/paper/2108.13665/) (2021)
- [SOTVerse: A User-defined Task Space of Single Object Tracking](https://mlsanity.com/paper/2204.07414/) (2022)
- [Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking](https://mlsanity.com/paper/2108.00187/) (2021)
- [Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics](https://mlsanity.com/paper/2201.13066/) (2022)
- [Benchmarking Visual Localization for Autonomous Navigation](https://mlsanity.com/paper/2203.13048/) (2022)
- [Mobile Vision Transformer-based Visual Object Tracking](https://mlsanity.com/paper/2309.05829/) (2023)
- [Detection-aware multi-object tracking evaluation](https://mlsanity.com/paper/2212.08536/) (2022)
- [New Performance Measures for Object Tracking under Complex Environments](https://mlsanity.com/paper/2111.07145/) (2021)
- [Probabilistic Tracking with Deep Factors](https://mlsanity.com/paper/2112.01609/) (2021)
- [Object Detection During Newborn Resuscitation Activities](https://mlsanity.com/paper/2303.07790/) (2023)
- [Every View Counts: View-Consistent Panoptic Quality for Multi-view Panoptic Segmentation](https://mlsanity.com/paper/2610.05911/) (2026)

## Abstract (arXiv)

The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments. This makes the cross-paper tracker comparison difficult. Furthermore, as some measures may be less effective than others, the tracking results may be skewed or biased towards particular tracking aspects. In this paper we revisit the popular performance measures and tracker performance visualizations and analyze them theoretically and experimentally. We show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others. Based on our analysis we narrow down the set of potential measures to only two complementary ones, describing accuracy and robustness, thus pushing towards homogenization of the tracker evaluation methodology. These two measures can be intuitively interpreted and visualized and have been employed by the recent Visual Object Tracking (VOT) challenges as the foundation for the evaluation methodology.