By Thierry Bouwmans, Fatih Porikli, Benjamin Höferlin, Antoine Vacavant
Background modeling and foreground detection are vital steps in video processing used to notice robustly relocating gadgets in tough environments. This calls for powerful equipment for facing dynamic backgrounds and illumination alterations in addition to algorithms that needs to meet real-time and occasional reminiscence requirements.
Incorporating either tested and new principles, Background Modeling and Foreground Detection for Video Surveillance provides an entire evaluate of the thoughts, algorithms, and purposes with regards to historical past modeling and foreground detection. Leaders within the box deal with quite a lot of demanding situations, together with digital camera jitter and history subtraction.
The publication provides the head tools and algorithms for detecting relocating items in video surveillance. It covers statistical versions, 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 history 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 innovations, this ebook is helping researchers and builders know the way to use historical past versions and foreground detection ways to video surveillance and comparable parts, corresponding to optical movement seize, multimedia functions, teleconferencing, video enhancing, and human–computer interfaces. it will probably even be utilized in graduate classes on laptop imaginative and prescient, picture processing, real-time structure, desktop studying, or information mining.
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Extra resources for Background Modeling and Foreground Detection for Video Surveillance
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. 4 Background Modeling and Foreground Detection for Video Surveillance Neural Network Models In this case, the background is represented by mean of the weights of a neural network suitably trained on N clean frames.
97] proposed to use a genetic K-means algorithm. The idea is to alleviate the disadvantages of the traditional K-means algorithm which has random and locality aspects causing lack of the global optimization. • Codebook models: Kim et al.  proposed to model the background using a codebook model. For each pixel, a codebook is constructed and consists of one or more codewords. Samples at each pixel are clustered into the set of codewords based on a color distortion metric together with brightness bounds.
This algorithm named SelfOrganizing Background Subtraction (SOBS) detects the moving object by using the background model through a map of motion and stationary patterns. Furthermore, an update neural network mapping method is used to make the neural network structure much simpler and the training step much more eﬃcient. Recently, Maddalena and Petrosino  improved the SOBS by introducing spatial coherence into the background update procedure. This led to the so-called SCSOBS algorithm, that provides further robustness against false detections.