Review and Evaluation of Commonly-Implemented Background Subtraction Algorithms

被引：153

作者：

Benezeth, Y. ^{[1
]}

Jodoin, P. M. ^{[2
]}

Emile, B. ^{[1
]}

Laurent, H. ^{[1
]}

Rosenberger, C. ^{[3
]}

机构：

[1] Univ Orleans, Inst PRISME, 88 Blvd Lahitolle, F-18020 Bourges, France

[2] Univ Sherbrooke, MOIVRE, Sherbrooke, PQ J1K 2R1, Canada

[3] Univ Caen, ENS, F-14000 Caen, France

来源：

19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 | 2008年

关键词：

D O I：

10.1109/icpr.2008.4760998

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Locating moving objects in a video sequence is the first step of man, computer vision applications. Among the various motion-detection techniques, background subtraction methods are commonly implemented, especially for applications relying on a fixed camera. Since the basic inter-frame difference with global threshold is often a too simplistic method, more elaborate (and often probabilistic) methods have been proposed. These methods often aim at making the detection process more robust to noise, back-ground motion and camera jitter In this paper, we present commonly-implemented background subtraction algorithms and we evaluate them quantitatively. In order to gauge performances of each method, tests are performed on a wide range of real, synthetic and semi-synthetic video sequences representing different challenges.

引用

页码：237 / +

页数：2