Background subtraction techniques: a review
Massimo Piccardi
- Vol. 4, pp 3099-3104
TLDR
A review of the main methods and an original categorisation based on speed, memory requirements and accuracy can effectively guide the designer to select the most suitable method for a given application in a principled way.Abstract:
Background subtraction is a widely used approach for detecting moving objects from static cameras. Many different methods have been proposed over the recent years and both the novice and the expert can be confused about their benefits and limitations. In order to overcome this problem, this paper provides a review of the main methods and an original categorisation based on speed, memory requirements and accuracy. Such a review can effectively guide the designer to select the most suitable method for a given application in a principled way. Methods reviewed include parametric and non-parametric background density estimates and spatial correlation approaches.read more
Citations
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Journal ArticleDOI
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
TL;DR: Efficiency figures show that the proposed technique for motion detection outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate.
Journal ArticleDOI
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
TL;DR: This work proposes an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science, that can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, and achieves robust detection for different types of videos taken with stationary cameras.
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High-throughput Ethomics in Large Groups of Drosophila
TL;DR: A camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in a planar arena finds that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype.
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Intelligent multi-camera video surveillance: A review
TL;DR: This paper reviews the recent development of relevant technologies from the perspectives of computer vision and pattern recognition, and discusses how to face emerging challenges of intelligent multi-camera video surveillance.
Proceedings ArticleDOI
CDnet 2014: An Expanded Change Detection Benchmark Dataset
TL;DR: The latest release of the changedetection.net dataset is presented, which includes 22 additional videos spanning 5 new categories that incorporate challenges encountered in many surveillance settings and highlights strengths and weaknesses of these methods and identifies remaining issues in change detection.
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Non-parametric Model for Background Subtraction
TL;DR: A novel non-parametric background model that can handle situations where the background of the scene is cluttered and not completely static but contains small motions such as tree branches and bushes is presented.