Exploring the Potential of Recurrence Quantification Analysis for Video Analysis and Motion Detection

This paper presents an enhanced methodology for Recurrence Quantification Analysis (RQA) designed specifically for video analysis. By utilizing image quality metrics, with a focus on the Peak Signal-to-Noise Ratio (PSNR), we determine meaningful values for the RQA threshold $\varepsilon$, a critical factor for successful image processing. Utilizing the False Nearest Neighbors (FNN) technique, we identify the optimal embedding dimension D for each patch within the video frames. Our approach produces a heatmap that visualizes temporal recurrence information for each video patch.