Change Detection in Video using Pixel based Parametric Analysis

Background/Objectives: This paper contains the change detection between frames of a video. The main objective is to show how the sequence of frames differ from one another in a video. Methods/Statistical Analysis: The amount of change between two frames is measured using the pixel based change detection parameter’s Absolute Average Difference (AAD), Peak Signal to Noise Ratio (PSNR), Normalized Correlation (NC), Signal to Noise Ratio (SNR) and Mean Squared Error (MSE). These parameters detect the changes in each and every pixel of the frame with respect to other frame and values are calculated for each frame of a video and compared in the tables followed by analysis of the values. Findings: The results indicated the changes that are not perceived by the human eyes. They depict that if the change between two frames are visible to the human eyes then they differ more based on the parameter we have considered and if the change is not visible to the eyes then minute differences is observed between the two respective frames. Application/Improvements: The change detection plays a significant role in visual media. Based on the change detection of frames, analysis of a video can be done.