By Thierry Bouwmans, Fatih Porikli, Benjamin Höferlin, Antoine Vacavant
Background modeling and foreground detection are vital steps in video processing used to discover robustly relocating gadgets in difficult environments. This calls for potent tools for facing dynamic backgrounds and illumination adjustments in addition to algorithms that needs to meet real-time and occasional reminiscence requirements.
Incorporating either tested and new rules, Background Modeling and Foreground Detection for Video Surveillance provides a whole review of the strategies, algorithms, and functions relating to history modeling and foreground detection. Leaders within the box tackle a variety of demanding situations, together with digicam jitter and history subtraction.
The booklet offers the head equipment and algorithms for detecting relocating items in video surveillance. It covers statistical types, clustering types, neural networks, and fuzzy types. It additionally addresses sensors, undefined, and implementation matters and discusses the assets and datasets required for comparing and evaluating heritage subtraction algorithms. The datasets and codes utilized in the textual content, in addition to hyperlinks to software program demonstrations, can be found at the book’s website.
A one-stop source on up to date types, algorithms, implementations, and benchmarking concepts, this publication is helping researchers and builders know how to use historical past versions and foreground detection the right way to video surveillance and similar components, akin to optical movement catch, multimedia functions, teleconferencing, video modifying, and human–computer interfaces. it could even be utilized in graduate classes on desktop imaginative and prescient, photograph processing, real-time structure, desktop studying, or facts mining.
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Extra info for Background Modeling and Foreground Detection for Video Surveillance
2011)  Original Codebook (CB) (1) Layered Codebook (LCB) (2) Hybrid Cone Cylinder Codebook (HCB) (2) Spherical Codebook (SCB) (1) Block based Codebook (BCB) (2) Hierarchical Codebook (HCB) (3) Multi-Scale Codebook (MCB) (1) Kim et al. (2003)  Kim et al. (2005)  Doshi and Trivedi (2006)  Hu et al. (2012)  Deng et al. (2008)  Guo and Hsu (2010)  Zaharescu and Jamieson (2011)  Basic Sequential Clustering (BSC) (2) Modified BSC (MBSC)) (2) Two-Threshold SC (TTSC) (1) Improved MBSC (IMBSC) (1) Xiao et al.
To address both limitations, Palomo et al.  proposed a growing hierarchical neural network. This neural network model has a hierarchical structure divided into layers, where each layer is composed of diﬀerent single SONNs with adaptative structure that is determined during the unsupervised learning process according to input data. Experimental results show good performance in the case of illumination changes. • ART-type (Adaptive Resonance Theory) Neural Network: Such networks exhibit two diﬀerent ﬁelds of neurons (without the input layer) that are bi-directional completely linked: the comparison ﬁeld and the recognition ﬁeld.
Support vector models: The second category uses more sophisticated statistical models as Support Vector Machine (SVM) , support vector regression (SVR)  and support vector data description (SVDD) . First, Lin et al.   proposed to initialize the background using a probabilistic Support Vector Machine (SVM). SVM classiﬁcation is applied for all pixels of each 1-20 Background Modeling and Foreground Detection for Video Surveillance training frame by computing the output probabilities.
Background Modeling and Foreground Detection for Video Surveillance by Thierry Bouwmans, Fatih Porikli, Benjamin Höferlin, Antoine Vacavant