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In the realm of industrial surveillance environments, Lyu et al. [2] proposed a visual early leakage detection system that employs an established background model to extract dynamic potential leakage foreground. Similarly, Li et al. [5] presented a novel approach to expedite the search process of surveillance video coding through the utilization of a background model. Their method involves an initial step of background modeling, followed by the implementation of "CU Classification" based on the established background. Wang et al. [6] emphasized the importance of surveillance video coding in improving compression efficiency in intelligent video surveillance systems and applications. They proposed a background modeling and referencing scheme for moving cameras-captured surveillance video coding in high-efficiency video coding (HEVC). The scheme includes a low-complexity motion background modeling algorithm for surveillance video coding and the use of motion background coding tree units (MBCTUs) to update the previous coding tree unit in the global compensation location of the background reference picture. Experimental results demonstrated significant bit savings of up to 26.6% and, on average, 6.7% with similar subjective quality and negligible encoding complexity compared to HM12.0. Tezcan et al. [7] introduced a new, supervised, background subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. They argued that the success of deep learning in computer vision did not bypass background subtraction (BGS) algorithm, which is founded on the results of background modeling. The diverse range of applications of background modeling technology in the domain of video processing has led to significant research efforts by scholars in this area