By Thierry Bouwmans, Fatih Porikli, Benjamin Höferlin, Antoine Vacavant
Background modeling and foreground detection are vital steps in video processing used to become aware of robustly relocating items in not easy environments. This calls for powerful tools for facing dynamic backgrounds and illumination adjustments in addition to algorithms that needs to meet real-time and occasional reminiscence requirements.
Incorporating either validated and new rules, Background Modeling and Foreground Detection for Video Surveillance provides an entire review of the innovations, algorithms, and purposes concerning heritage 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 ebook offers the pinnacle tools and algorithms for detecting relocating items in video surveillance. It covers statistical versions, clustering versions, neural networks, and fuzzy versions. 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, besides 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 ideas, this publication is helping researchers and builders know the way to use historical past versions and foreground detection how to video surveillance and comparable components, reminiscent of optical movement seize, multimedia functions, teleconferencing, video modifying, and human–computer interfaces. it may possibly even be utilized in graduate classes on laptop imaginative and prescient, snapshot processing, real-time structure, laptop studying, or info mining.
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Additional info for Background Modeling and Foreground Detection for Video Surveillance
Then, it is impossible to compute a representative background image. • Camouflage: A foreground object’s pixel characteristics may be subsumed by the modeled background. Then, the foreground and the background cannot be distinguished. b shows an illustration of camouﬂage in color. A person enters the scene with a suitcase and leaves it on the ﬂoor. The diﬃculty is the similar color between the suitcase and the ﬂoor. 12 show a camouﬂage in color and in depth, respectively. • Foreground aperture: When a moved object has uniform colored regions, changes inside these regions may not be detected.
So, each cluster contains pixels that have similar features in the HSV space color. Then, the background model is applied on these clusters to obtain cluster of pixels classiﬁed as background or foreground. This cluster-wise approach gives less false alarms. Instead of the block-wise approach, the foreground detection is obtained with a pixel-wise precision. Finally, the size of the element determines the robustness to the noise and the precision of the detection. A pixel-based method gives a pixel-based precision but it is less robust to noise than block-based or cluster-based methods.
This updating method is simple, eﬃcient and can be used in real-time detection systems. • Correntropy filter: The Kalman ﬁlter gives the optimal solution to the estimation problem when all the processes are Gaussian random processes and then KF oﬀers a sub-optimal behavior in non-Gaussian settings which is the case in some challenging situations met in video-surveillance. So, Cinar and Principe  proposed a Correntropy ﬁlter (CF) that extract higher order information from the sequence. The information theoretic cost function is based on the similarity measure Correntropy.