# Summarizing the performances of a background subtraction algorithm measured on several videos

arXiv: https://arxiv.org/abs/2002.05654 · HTML: https://mlsanity.com/paper/2002.05654/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Foundations of the Theory of Performance-Based Ranking](https://mlsanity.com/paper/2412.04227/) (2024)
- [An exploration of the performances achievable by combining unsupervised background subtraction algorithms](https://mlsanity.com/paper/2202.12563/) (2022)
- [A Methodology to Evaluate Strategies Predicting Rankings on Unseen Domains](https://mlsanity.com/paper/2505.15595/) (2025)
- [Multi-domain performance analysis with scores tailored to user preferences](https://mlsanity.com/paper/2512.08715/) (2025)
- [Event Detection in Videos: A Framework for the Development of New Methods](https://mlsanity.com/paper/2607.04372/) (2026)

## Abstract (arXiv)

There exist many background subtraction algorithms to detect motion in videos. To help comparing them, datasets with ground-truth data such as CDNET or LASIESTA have been proposed. These datasets organize videos in categories that represent typical challenges for background subtraction. The evaluation procedure promoted by their authors consists in measuring performance indicators for each video separately and to average them hierarchically, within a category first, then between categories, a procedure which we name "summarization". While the summarization by averaging performance indicators is a valuable effort to standardize the evaluation procedure, it has no theoretical justification and it breaks the intrinsic relationships between summarized indicators. This leads to interpretation inconsistencies. In this paper, we present a theoretical approach to summarize the performances for multiple videos that preserves the relationships between performance indicators. In addition, we give formulas and an algorithm to calculate summarized performances. Finally, we showcase our observations on CDNET 2014.