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20202026
most citedMixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillance

8 citations · 16 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

Event Detection in Videos: A Framework for the Development of New Methods

Anastasia Zakharova, Thierry Bouwmans, Anthony Cioppa +13

Event detection tasks in videos, the most important aspect of video surveillance, aim to detect events either at the pixel-level, frame-level, or clip-level. Plenty of methods inte…

cs.CV2024★ 1 cited

The Tile: A 2D Map of Ranking Scores for Two-Class Classification

Sébastien Piérard, Anaïs Halin, Anthony Cioppa +2

In the computer vision and machine learning communities, as well as in many other research domains, rigorous evaluation of any new method, including classifiers, is essential. One…

cs.CV2024★ 1 cited

A Hitchhiker's Guide to Understanding Performances of Two-Class Classifiers

Anaïs Halin, Sébastien Piérard, Anthony Cioppa +1

Properly understanding the performances of classifiers is essential in various scenarios. However, the literature often relies only on one or two standard scores to compare classif…

cs.CV2024★ 1 cited

Physically Interpretable Probabilistic Domain Characterization

Anaïs Halin, Sébastien Piérard, Renaud Vandeghen +10

Characterizing domains is essential for models analyzing dynamic environments, as it allows them to adapt to evolving conditions or to hand the task over to backup systems when fac…

cs.CV2022★ 8 cited

Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillance

Sébastien Piérard, Anthony Cioppa, Anaïs Halin +5

Various tasks encountered in real-world surveillance can be addressed by determining posteriors (e.g. by Bayesian inference or machine learning), based on which critical decisions…

cs.CV2022★ 1 cited

An exploration of the performances achievable by combining unsupervised background subtraction algorithms

Sébastien Piérard, Marc Braham, Marc Van Droogenbroeck

Background subtraction (BGS) is a common choice for performing motion detection in video. Hundreds of BGS algorithms are released every year, but combining them to detect motion re…

cs.CV2020★ 1 cited

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

Sébastien Piérard, Marc Van Droogenbroeck

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.…