Fitting and Filtering Functional Data for Use in Video Data Analysis

Our research focuses on advancing the capabilities of machine learning applications that involve analyzing video data. To achieve this, we have created a novel method for integrating functional data into video. Our approach entails the direct application of convolutional filters to functional data, as well as the introduction of new filters that make use of derivatives, which represent an exciting avenue for further exploration. In order to validate the effectiveness of our approach, we conducted experiments using both synthetic and real-world datasets. These experiments helped us establish our method’s potential in practical scenarios.We propose a specific parameter ratio for incorporating functional data into the original input frames. This parameter ratio has been shown to require less information while offering substantial potential for exploration within the realm of machine-learning applications for video data. Furthermore, we found that additional operations applicable to functions, such as derivatives, yield valuable information that can be harnessed to enhance machine learning applications involving video data. This opens up exciting possibilities for leveraging the richness of functional data in video analysis.