## 4 Experimental Results and Discussion
### Third Experimental Scenario: Video-Surveillance Data  
In this experiment, to evaluate and compare the performance of our proposed approach using WOA (FS-WOA) with established methods - Single, KNN, MICE, and GAIN - we investigate imputation methods applied to video-surveillance datasets. In smart cities, complete datasets are very important to be used in traffic surveillance video analysis. This experiment aims to evaluate the effectiveness of imputation methods in reconstructing missing data in traffic surveillance datasets and thereby contribute to the development of techniques that improve the reliability and interpretability of surveillance data. Data were collected using a network of 6 cameras, comprising 4 static and 2 PTZ (pan-tilt-zoom) units. The cameras were strategically positioned to cover key areas within the observation environment, ensuring comprehensive visibility. Static cameras were mounted at fixed angles to capture continuous footage, while PTZ cameras were deployed to monitor dynamic areas, allowing for adjustable angles and zoom capabilities. This simple configuration facilitated the collection of diverse data on object movements and environmental conditions across various scenarios. The video files were recorded in MP4 format, which is widely supported and facilitates efficient storage and playback. To extract tabular data from these video files, we employed a computer vision algorithm that analyzes the footage to detect and track objects. This process involved applying object detection techniques and frame-by-frame analysis to generate structured data, including object types, velocities, and actions, which were then compiled into a comprehensive dataset for further analysis. In total, five distinct tabular datasets were extracted from the traffic surveillance videos for this study, each presenting unique challenges and complexities. These variations are outlined in detail in Table 7.  
The datasets encompass various features that offer valuable insights into the characteristics and dynamics of the observed objects and their environment, detailed as follows:  
* **Camera IDs**: This feature denotes the unique identifier assigned to each camera that records the video stream, allowing for easy tracking of which camera captured specific data instances. * **Object type**: This feature categorizes the different types of objects present in the video stream, including classifications such as 'person','vehicle', 'animal', and more. * **Object velocities**: This attribute indicates the speeds of the objects detected in the video stream, encompassing details about their direction and rate of movement. * **Object actions**: This feature outlines the behaviors or actions performed by the identified objects, including activities such as \"walking\", \"running\", \"turning\", and \"stopping\". \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Single** & **KNN** & **MICE** & **GAIN** & **FS-WOA** \\ \hline \multicolumn{5}{c}{**Missing rate 25\%**} \\ \hline Iris & 0.2153 (5.0) & 0.0938 (4.0) & **0.0451 (1.0)** & 0.0839 (3.0) & 0.0543 (2.0) \\ Breast cancer & **0.1830 (1.0)** & 0.1942 (2.0) & 0.2183 (4.0) & 0.2902 (5.0) & 0.1953 (3.0) \\ Wine & 0.2442 (5.0) & 0.1792 (3.0) & 0.1712 (2.0) & 0.1982 (4.0) & **0.1440 (1.0)** \\ Digits & 0.2324 (3.0) & 0.2315 (2.0) & 0.2785 (5.0) & 0.2780 (4.0) & **0.1471 (1.0)** \\ Diabetes & 0.2464 (4.0) & 0.2873 (5.0) & 0.2195 (2.0) & 0.2250 (3.0) & **0.1957 (1.0)** \\ Ionosphere & 0.2590 (2.0) & **0.2507 (1.0)** & 0.3497 (4.0) & 0.4827 (5.0) & 0.2928 (3.0) \\ Sonar & 0.2349 (2.0) & **0.2286 (1.0)** & 0.2883 (4.0) & 0.3779 (5.0) & 0.2547 (3.0) \\ Vowel & 0.2371 (5.0) & **0.1048 (1.0)** & 0.2021 (3.0) & 0.2048 (4.0) & 0.1158 (2.0) \\ Vehicle & 0.2536 (5.0) & 0.0762 (3.0) & **0.0507 (1.0)** & 0.0841 (4.0) & 0.0530 (2.0) \\ Mnist & 0.2322 (3.0) & 0.2148 (2.0) & 0.2754 (4.0) & 0.2798 (5.0) & **0.1082 (1.0)** \\ \hline Average ranks & 3.5 & 2.4 & 3.0 & 4.2 & **1.9** \\ \hline \hline \end{tabular}
\end{table}