# An Effective Motion-Tracking Scheme for Machine-Learning Applications in Noisy Videos
## 4 Evaluation  
In this section, we present experimental results to evaluate the proposed scheme. For the algorithm performance evaluation, AMD's Ryzen 5 5600X was used, and all codes were written using Python and OpenCV. In addition, 40 videos of similar types with noise collected for performance tests were classified and utilized to proceed with each test. As a method of evaluating our proposed algorithm, the following method was used. The most widely used, precision-recall, was used for object detection. The object-tracking part calculates the ratio of how much the tracker maintains tracking and catches the wrong object in the entire frame, converts it into a percentage, and expresses it as the loss. For the object detection verification method, a random frame was selected from the entire image and the result obtained through the algorithm was compared with the result directly detected by a person and verified. In the case of tracking, a black square box was drawn at the coordinates where the tracker was located on the algorithm result screen to check if there was an error in the square. Figure 9 is a picture comparing the resulting image created by the algorithm with the red square box image drawn by a person for three of the images used for verification. We drew a square box to make tracking verification easier. The full verification result is shown in Table 3 below. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Video Number** & **Precision** & **Recall** & **Loss** \\ \hline
1 & 0.95 & 0.96 & 0.08 \\
2 & 0.96 & 0.98 & 0.09 \\
3 & 0.95 & 0.95 & 0.06 \\
4 & 0.98 & 0.97 & 0.06 \\
5 & 0.97 & 0.96 & 0.01 \\
6 & 0.99 & 0.95 & 0.01 \\
7 & 0.97 & 0.98 & 0.08 \\
8 & 0.96 & 0.97 & 0.08 \\
9 & 0.95 & 0.92 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision-recall of object detection and loss-of-object tracking. Figure 9: Comparison of the square box image drawn by a person and the proposed scheme. The strength of our proposed algorithm is that, when compared with the validation part of other reference papers, the same results were obtained for the same input, and even faster results were obtained without using a predefined training dataset. Table 4 below summarizes the time it took for our proposed algorithm to detect an object and output the result to the screen. The number of detected objects according to the time taken for each frame was written, and the time was written up to the second decimal place. In the case of the first frame, it took some time while reading the video, and there were some irregular measurements in the middle. However, it can be seen that on average, a very small time of 0.02 s was required. Figure 10 is the result of comparing the performance of the proposed scheme with other algorithms. In each proposed method, the characteristics of the object to be detected are different. However, when trying to detect a large number of objects, comparing the time required to derive a result is very meaningful for the performance verification of the proposed method. As a comparison criterion, the computation time required to detect multiple objects was compared. In the reference comparing the cases of F-RCNN(1) and YOLO, it was mentioned that each method takes 32 fps and 20 fps [5]. When 1 s is 30 fps, it can be seen that 32 fps and 20 fps are converted to 1.06 s and 0.66 s, respectively. For R-CNN, it was stated that the average detection time of the photos used in the test was 0.14 s, but it was only written for a single object [16]. When F-RCNN(2) was used, it was said that particles with a higher mobility were detected within 1.12 s [15]. Lastly, our proposed algorithm had an