Advanced background subtraction approach using Laplacian distribution model

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. Our proposed block alarm module efficiently checks each block for the presence of either 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 found to contain background pixels. For our proposed background modeling module, a unique two-stage background training procedure is performed using Rough Training followed by Precise Training in order to generate a high-quality adaptive background model. As the final step of our process, we present an object extraction module which will compute the binary object detection mask through the applied suitable threshold value. This is accomplished by using our proposed threshold training procedure in order to achieve accurate and complete detection of moving objects. The overall results of these analyses demonstrate 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 57.17% and 48.48%, respectively.