Methodology for analysis of content transfer kinetics. In our methodology for analyzing video data, we employ a multi-faceted approach that primarily revolves around detecting and quantifying pixel intensity variations over time. Initially, the video frames are extracted from .tif files and subjected to preprocessing. This preprocessing involves using a 9×9 averaging filter to smooth pixel values, which significantly enhances the accuracy of our subsequent analyses. This step plays a crucial role in noise reduction, contributing to the improved fidelity of event detection. To further enhance the accuracy of our analysis, we exclude the 3 layers of pixels very close to the edges of the images, as any event occurring there may not be fully captured. Following the initial smoothing step, we implement a multi-threshold strategy to identify substantial changes in pixel intensity across frames. These changes serve as indicators of events of interest. To further refine our data, we incorporate various filtering techniques. This includes the removal of redundant and adjacent pixel events to ensure the independence of detected events. Additionally, we apply a low-pass filter to smooth the signal, facilitating the identification of more subtle changes in intensity. This will be used later with the MATLAB function "findpeaks." By utilizing this function, we can determine the location of peak maxima, their prominence, and their width. Peaks that certain arbitrarily defined thresholds in terms of width and height are considered as fusion events. Once the events are detected, we calculate a cumulative distribution function (CDF) of the wait times.