A Study of Illumination Filtering and Movement Detection in Video Streaming Processing

We study a movement detection scheme for video streaming data using time series analysis. We employ simple scalar quantities such as (i) the Frobenius norm trajectory of a forward difference matrix and (ii) the Auto-correlation function to analyze the time series and detect movements in the video sequences. To remove the background interruptions, such as the change of lights, we study the performance of two filters, (1) SVD filter and (2) SVD combined with the Butterworth filter, to repress data disruptions. From the example, we identify the best filter and the best statistical quantity to detect movement in the disrupted data. By using the background subtraction filters as well as the Frobenius norm trajectory of data, we can distinguish the movements and light disruptions at various time points in the video sequences. The combo filter is found to be the best as it completely removes the light interruption while maintaining the movements data unaffected and smooths other static artifacts.