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Background Modeling and Foreground Detection for Video Surveillance

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algorithm
Background Modeling
background models and foreground detection methods
Background Pixel
Background Subtraction
Background Subtraction Algorithms
Background Subtraction Techniques
backgrounds
BS Method
camera
Camera Jitter
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computer vision methods
dynamic
Dynamic Background
dynamic backgrounds and illumination changes in video surveillance
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Foreground Detection
Foreground Mask
Foreground Object Detection
Foreground Objects
Foreground Pixel
Foreground Regions
gaussian
Gaussian Mixture Model
GPU implementations of methods
HSV Color Space
jitter
Kernel Density Estimation
Low Rank Matrix
methods and algorithms for detecting moving objects in video surveillance
mixture
motion detection algorithm benchmarking
Motion Detection Methods
Motion Segmentation
neural network models
Non-Parametric Kernel Density Estimation
objects
pixels
real-time video systems
RGB Color Space
Rough Set Theory
sensor data processing
statistical image analysis
Subspace Tracking
subtraction
SVDD
video analytics techniques

Product details

  • ISBN 9781482205374
  • Weight: 1292g
  • Dimensions: 178 x 254mm
  • Publication Date: 25 Jul 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements.

Incorporating both established and new ideas, Background Modeling and Foreground Detection for Video Surveillance provides a complete overview of the concepts, algorithms, and applications related to background modeling and foreground detection. Leaders in the field address a wide range of challenges, including camera jitter and background subtraction.

The book presents the top methods and algorithms for detecting moving objects in video surveillance. It covers statistical models, clustering models, neural networks, and fuzzy models. It also addresses sensors, hardware, and implementation issues and discusses the resources and datasets required for evaluating and comparing background subtraction algorithms. The datasets and codes used in the text, along with links to software demonstrations, are available on the book’s website.

A one-stop resource on up-to-date models, algorithms, implementations, and benchmarking techniques, this book helps researchers and industry developers understand how to apply background models and foreground detection methods to video surveillance and related areas, such as optical motion capture, multimedia applications, teleconferencing, video editing, and human–computer interfaces. It can also be used in graduate courses on computer vision, image processing, real-time architecture, machine learning, or data mining.
About

Thierry Bouwmans, Fatih Porikli, Benjamin Höferlin, Antoine Vacavant