Traditional and recent approaches in background modeling for foreground detection: An overview

By Thierry Bouwmans. Computer Science Review, 2014

690 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c39cd8b0fac1e78e71674edaeceb3d9f2c974913

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System (2018, background): “For environments where chroma keying is not possible, there are a large number [82] of background subtraction algorithms.”
Robust Spectral Filtering and Anomaly Detection (2018, neutral): “Further, we should like to point to the closely related problems of deviation [27], or (on-line, complex) event [40] detection, outlier analysis [2], detection [33], or pursuit [39], foreground detection [8] and the complementary background subtraction or background maintenance, or even dynamic anom”
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation (2018, background): “In the last two decades, background subtraction for video taken by static cameras has been one of the most active research topics in computer vision due to a big amount of applications including intelligent surveillance as human activities in public spaces, traffic monitoring and industrial machine ”
Camera-trap images segmentation using multi-layer robust principal component analysis (2017, background): “Bouwmans [2] provides a complete survey of the traditional and recent approaches on background subtraction.”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, background): “To have an overview about the fundamental research in this field, the reader can refer to numerous surveys on background initialization [235][236][47][171] and background subtraction methods [46][43][128][45][41][42][50][44][49].”
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015, methodology): “Many foreground detection methods have been developed [33][29][30][31][34][321], and several implementations are available in the BGS Library [346].”
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, background): “There is a large corpus of works related to background subtraction that originated from the computer vision community (see [63] for a review).”
Deep background subtraction with scene-specific convolutional neural networks (2016, methodology): “To address the complexity of dynamic scenes, most researchers have worked on developing sophisticated background models such as Gaussian mixture model [1], kernelbased density estimation [2] or codebook construction [3] (see [4], [5] for reviews on background subtraction).”