## 4 Experimental Results and Discussion
### Third Experimental Scenario: Video-Surveillance Data

Table 6: _Cont._* **Environmental conditions**: This encompasses relevant environmental factors captured in the video stream, such as lighting conditions and weather elements. * **Object sizes**: This indicates the dimensions of the objects identified in the video stream, which may include metrics like height, width, or area. * **Object acceleration**: This attribute describes the rate of acceleration of the detected objects. * **Object distance to camera**: This feature measures the distance between the detected objects and the camera, providing spatial context about an object's position relative to the camera. * **Lighting conditions**: This feature details the illumination present in the video stream, covering brightness levels and various lighting scenarios such as \"daylight\", \"low light\", and \"artificial light\". * **Crowd Density**: This feature quantifies the number of objects within the camera's field of view, offering insights into the presence of individuals in a specific area or frame of video. It categorizes crowd density into levels such as \"sparse\", \"moderate\", and \"dense\". As mentioned earlier, all the data imputation methods, including our proposed imputation approach, were applied to the selected traffic monitoring datasets to check their performance on this type of data with different rates of missing data. To determine which of the data imputation methods is suitable for traffic monitoring datasets, check the results shown in Table 8 underlined:  
* With a missing-data rate of 5%, the proposed imputation approach using WOA (FS-WOA) gave the best result with four of the five datasets used and the second-best result with the fifth dataset. Although the MICE method ranked first on only one of the five datasets, it ranked second on all the other datasets. Overall, the MICE method ranked second after the FS-WOA approach with an average rank of 1.8. KNN was third, GAIN was fourth, and the Single method was the worst. * The proposed imputation approach using WOA (FS-WOA) outperformed all other approaches on all traffic monitoring datasets with missing-data rates of 15% and 25%. The MICE method performed very well on all datasets and reached the second position with an average rank of 2. GAIN reached the third position on average, followed by the KNN method and the Single method in the last position. The superiority of the FS-WOA approach over all other methods in perfectly handling missing-data problems in traffic monitoring demonstrates that the proposed approach can consistently deliver high-quality imputations in a wide range of traffic monitoring scenarios. Furthermore, the FS-WOA approach achieved promising performance even with high rates of missing data. This indicates that our proposed data imputation approach (FS-WOA) is significantly the best choice in the field of traffic monitoring and other applications due to its stability and adaptability, even with high rates of missing data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **\# Data Points** & **\# Features** \\ \hline VSD\({}_{1}\) & 500 & 6 \\ VSD\({}_{2}\) & 800 & 7 \\ VSD\({}_{3}\) & 1000 & 6 \\ VSD\({}_{4}\) & 650 & 8 \\ VSD\({}_{5}\) & 1000 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Description of the Video-Surveillance Datasets. The datasets extracted from the video stream are tabular numeric data.