Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

Abstract

<p>In the computer vision, background extraction is a promising technique. It is characterized by being applied in many different real time applications in diverse environments and with variety of challenges. Background extraction is the most popular technique employed in the domain of detecting moving foreground objects taken by stationary surveillance cameras. Achieving high performance is required with many perspectives and demands. Choosing the suitable background extraction model plays the major role in affecting the performance matrices of time, memory, and accuracy.</p><p>In this article we present an extensive review on background extraction in which we attempt to cover all the related topics. We list the four process stages of background extraction and we consider several well-known models starting with the conventional models and ending up with the state-of-the art models. This review also focuses on the model environments whether it is human activities, Nature or sport environments and illuminates on some of the real time applications where background extraction method is adopted. Many challenges are addressed in respect to environment, camera, foreground objects, background, and computation time. </p><p>In addition, this article provides handy tables containing different common datasets and libraries used in the field of background extraction experiments. Eventually, we illustrate the performance evaluation with a table of the set performance metrics to measure the robustness of the background extraction model against other models in terms of time, accurate performance and required memory.</p>

Algorithm of CAD Surface Generation for Complex Pipe Model in Industry 4.0 Background

“…In a complex pipeline video surveillance system, when the surveillance camera oscillates slightly, the captured complex pipeline video pixels will not follow the normal distribution, and the above method will cause the extracted complex pipeline video background confusion [ 23 ]. Based on the normal distribution, a coefficient α representing the speed of pixel update is introduced to control the proportion of complex pipeline video background.…”

Efficient Hybrid Classification Approach for COVID-19 Based on Harris Hawks Optimization and Salp Swarm Optimization

“…true negatives) corresponds to the negatives COVID-19 images that have been successfully labeled by the classifier. False positives (FP) are positive COVID-19 images mislabeled as negative, whereas false negatives (FN) are negative COVID-19 images that have been incorrectly identified as positive COVID-19 images [29].…”

Comparative Analysis of Background Subtraction Models Applied on a Local Dataset Using a New Approach for Ground-truth Generation

“…( 6) is the number of foreground pixels that are misclassified as background pixels, and Percentage of Wrong Classifications (PWC) in Eq. ( 7) indicates the error rate which is the percentage of misclassified pixels to the original pixels [3].…”

“…Through decades, many background subtraction models have been developed and introduced to tackle the background subtraction challenges, these models are classified into different categories by several surveys and review articles, [3] [4]. Basic models are the simplest models they depend on a threshold to decide whether it's a foreground or background pixel and it is more convenient to models with a single background distribution [5], mean model [6], median model [7], and histogram analysis model [8] are examples of the basic models.…”

“…Gaussian Mixture Model (GMM) [15] is example of parametrical statistical models [16]. On the other hand, Visual Background extractor (ViBe) [17], Substance Sensitivity Segmenter (SuBSENSE) algorithm, kernel density estimation (KDE) [18] and fuzzy models [19] are examples of non-parametrical statistical models [3].…”