SIMILARITY CHECKING OF CCTV IMAGES USING PEARSON CORRELATION: IMPLEMENTATION WITH PYTHON

Abstract

Video surveillance technology, such as CCTV, is increasingly common in various applications, including public safety and business surveillance. Analyzing and comparing images from CCTV systems is essential for ensuring safety and security. This research implements the Pearson Correlation method in Python to measure the similarity of CCTV images. Pearson Correlation, which assesses the linear relationship between two variables, is employed to compare the pixel values of two images, resulting in a coefficient that indicates the degree of similarity. We used a quantitative approach with experiments on two scenarios to test the program's effectiveness in measuring image similarity. The results demonstrate that Pearson Correlation is highly effective in distinguishing between identical and other images, providing a more accurate and comprehensive assessment of image similarity compared to histogram analysis. However, the findings are constrained by the specific scenarios and dataset utilized. Further research with more diverse empirical data is required to generalize these results across a broader range of CCTV conditions.