Enhancing Data Quality in Smart Video Surveillance Systems: A Whale Optimizer-Based Imputation Algorithm

One of the most challenging aspects of data analysis, especially in video surveillance, is missing data. Missing data can lead to biased conclusions and reduced statistical power, thus affecting the accuracy and reliability of analyses. By introducing an optimization-oriented approach to data imputation, this study addresses the problem of missing data in tabular data, which is suitable for many applications. We propose a novel algorithm, the Whale Optimizer-based Imputation Algorithm (WO-IA), which uses optimization techniques to select more effective features for imputation while ignoring less important features. This strategic feature selection improves the accuracy of the imputation results and increases the efficiency of t he computations even with high missing rates. To evaluate the performance of our proposed novel approach (WO-IA), we compare it with several state-of-the-art missing value imputation methods in different experimental scenarios on synthetic and video surveillance datasets with different missing value rates. The results show that WO-IA is robust and stable, making it a reliable choice for missing data imputation in different scenarios. These results demonstrate the effectiveness of the algorithm in improving the accuracy and reliability of video surveillance systems.