Subspace Learning for Background Modeling: A Survey

Thierry Bouwmans (1)

1 MIA - Mathématiques, Image et Applications - EA 3165

Background modeling is often used to detect moving object in video acquired by a fixed camera. Recently, subspace learning methods have been used to model the background in the idea to represent online data content while reducing dimension significantly. The first method using Principal Component Analysis (PCA) was proposed by Oliver et al. and a representative patent using PCA concerns the detection of cars and persons in video surveillance. Numerous improvements and variants were developed over the recent years. The purpose of this paper is to provide a survey and an original classification of these improvements. Firstly, we classify the improvements of the PCA in term of strategies and the variants in term of the used subspace learning algorithms. Then, we present a comparative evaluation of the variants and evaluate them with the state-of-art algorithms (SG, MOG, and KDE) by using the Wallflower dataset.

Mots clés: Background modeling; Subspace Learning; Principal Components Analysis

Domaines: Traitement des images [eess.IV]

Thierry Bouwmans. Subspace Learning for Background Modeling: A Survey. Recent Patent On Computer Science, 2009, 2 (3), pp.223-234. ⟨hal-00534555⟩