PROJECT TITLE :
Background subtraction using semantic-based hierarchical GMM
Background as well as a protracted-amount quick illumination variation is commonly assumed to be foreground by mistake. To resolve this downside, proposed may be a semantic-primarily based hierarchical Gaussian mixture model integrated with an illumination detection approach. Initial, autocorrelation- based options for broad identification of background lighting changes and foreground in short-term sequences are presented. Then, the hierarchical Gaussians representing completely different background illumination variations are maintained. The effectiveness of the proposed methodology is demonstrated using experiments on pedestrian detection in quick lighting amendment.
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