Regression Analysis of Ghosting Artifacts in Temporal Fusion Videos Using AutoML

Temporal Fusion Video (TFV) is a technique that amalgamates multiple frames in temporal sequence to generate a cohesive video output, thereby facilitating various applications such as motion analysis, scene summarization, and the creation of unique visual effects. Despite its advantages in capturing fast-moving objects and efficiently summarizing long-duration changes, TFV is susceptible to ghosting artifacts-transparent overlapping images resulting from frame blending. These arti-facts compromise visual quality and hinder accurate analysis. This paper aims to mitigate the challenge posed by ghosting artifacts by proposing an AI model for quantifying them through measurement and regression analysis, with a specific focus on leveraging Automated Machine Learning (AutoML). Our methodology seeks to identify the key factors contributing to ghosting artifacts and model their impact systematically, thus enabling the development of targeted strategies for artifact reduction. The implications of our research are profound, as it offers a quantitative foundation for evaluating TFV methods, thereby enhancing standardization within the field. Moreover, our approach holds promise for improving the practical applications of TFV in domains such as surveillance, entertainment, and sci-entific visualization. By bridging the existing gap in research, our findings introduce a novel and objective method for enhancing the visual quality and applicability of TFV techniques.