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In this paper, we propose a novel background subtraction approach in order to accurately detect moving objects. Our method involves three important proposed modules: a block alarm module, a background modeling module, and an object extraction module. The block alarm module efficiently checks each block for the presence of either a moving object or background information. This is accomplished by using temporal differencing pixels of the Laplacian distribution model and allows the subsequent background modeling module to process only those blocks that were found to contain background pixels. Next, the background modeling module is employed in order to generate a high-quality adaptive background model using a unique two-stage training procedure and a novel mechanism for recognizing changes in illumination. As the final step of our process, the proposed object extraction module will compute the binary object detection mask through the applied suitable threshold value. This is accomplished by using our proposed threshold training procedure. The performance evaluation of our proposed method was analyzed by quantitative and qualitative evaluation. The overall results show that our proposed method attains a substantially higher degree of efficacy, outperforming other state-of-the-art methods by Similarity and F1 accuracy rates of up to 35.50% and 26.09%, respectively.