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>
Highlights
In the last two decades and with the rapid development of sensors and the increasing safety concerns, detecting moving objects has been one of the most essential topics in iJOE ‒ Vol 17, No 02, 2021the computer vision field [1]
Sometimes it's needed to check on the numbers products left on the shelves [143], such information could be provided by the Radio Frequency Identification (RFID) tag [160] which normally attached to the product itself, but it is not always the best choice in terms of cost and time needed to attach the tag to each product
We reviewed the background extraction models used to detect the moving foreground target in a video taken by a surveillance stationary camera
Summary
In the last two decades and with the rapid development of sensors and the increasing safety concerns, detecting moving objects has been one of the most essential topics in iJOE ‒ Vol 17, No 02, 2021. Real time applications with different environment and many challenges are available with diverse interest where the foreground data can be used in tracking, synopsis and anomaly detection [2][3][4]. Background extraction is the most popular technique employed in this domain to extract the foreground moving objects taken by stationary surveillance camera [7]. In section 6&7 we list tables of datasets and libraries used in the field of background extraction model and section 8 is illustrating the performance evaluation and the set performance metrics to assess the background extraction models in terms of time, accuracy and memory
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