## 5 Experiments and Results  
The proposed method was implemented in C++ language, and test runs were made on a personal computer with an i7-6500U processor with 8 GB memory and a Windows 10 operating system. First, we conducted a virtual test to determine the optimal parameters for the proposed background classification algorithm's variables. The synthetic images were created by the 3 ds Max 2017 program developed by Autodesk. A model car was scanned by the commercial scanner REXCAN4(r) developed by Medit; this 3D model and the road view image texture were applied to the 3 DS Max virtual space. Figure 9 shows the synthetic image example we used for the experiment. In the background classification process, several variables can affect the performance of the classification. Since it uses feature point matching, the resolution of the image can affect the matching result. The high resolution image includes the details of the image, while the low resolution image can extract the sharp feature points [30]. Furthermore, the coefficient for the SIFT algorithm can affect the results. If the coefficient (threshold) moves higher, the quantity of the matching points might be small. Otherwise, if the coefficient is lowered, the quantity of the matching points might be large, including the false matching points. The threshold distance between the feature point and the epipolar line can also affect the results. The repeated classification processes can make the classification results more accurate, but a large volume of iterations may cause a high computational cost. These parameters have been optimized from the virtual data test. One hundred arbitrary synthetic images with \(800\times 600\) resolution were captured for the test. The camera calibration data and IMU data are obtained easily from the designed synthetic data. The background classification process was applied to each image with differing variables. The feature points were classified manually and used as the ground truth. The average error rates of the experimental results are shown in Figure 10.  
The best result was with 70% resolution, 10.0 for the SIFT coefficient and 1.0 pixel for the point-line distance threshold. Three iterations were enough to make the classification error converge to the minimum. We take a simple test with the real-world images comparing these variables, and it shows identical results. Therefore, we used these parameters in the next experiment with real-world images. There are also interesting results in the angle difference of the moving object direction and the moving camera direction. The angle of camera's moving direction and the object's moving direction affects the convergence speed of the algorithm. If the camera and the object are moving in the same direction, the iteration number to the convergence becomes larger. This is because the motion of the feature points on the object pretends to be the background motion caused by the egomotion. On the other hand, if the camera and the object are moving to the different direction, the convergence speed gets faster. This is because the feature point motion is amplified to be easily recognized. The tested result of convergence iteration affected by the moving direction difference is shown in Figure 11.  
Figure 9: Virtual test configuration. (**a**) Scanned model car; (**b**) synthetic image modeling by 3 DS Max. For the real-world experiment, we used the commercial industrial camera uEye CP developed by IDS and the IMU sensor LPMS-B2 developed by LP-Research. We designed and 3D printed a frame to assemble the two sensors in a fixed position. The configuration of the experiment equipment is shown in Figure 12.  
Figure 11: Iterations required for the convergence affected by the direction difference of moving object and camera. Figure 10: The result of the synthetic data test. (**a**) Resolution variation; (**b**) SIFT coefficient variation; (**c**) point-line distance threshold variation; (**d**) classification iteration variation.