## 4 Results and Discussion
### Experimental Results  
Figure 9 shows the snapshots of the robot's view in an experiment. The green and red rectangles around the persons indicate the target person and the other persons, respectively. The white rectangle on the target person represents the color detection, while the blue rectangle indicates the ROI on the target person. The ROI was used to ensure that the color was extracted only from the person's clothes. The ROI is especially important in a colorful environment like ours, that is where there were black, white, green, yellow, orange, and blue scenes. The robot started to follow its target from the laboratory to the end of the corridor based on the color feature in the indoor environment. Initially, the color feature was extracted and the distance of the target estimated simultaneously by the RGB-D camera. The system identified the target person wearing the white t-shirt as the target and another person who stood up in the middle of the laboratory as a non-target. Then, the robot started to follow the target person (Figure 9a). The robot continued to follow the person from the departure position toward the destination position. After a few seconds, the target turned left by approximately 90\({}^{\circ}\) at the first corner (Figure 9b). We call this moment the LOP (Figure 6a). The target suddenly disappeared from the FoV of the camera when he/she was in the corridor and the robot was still in the laboratory (Figure 9c). Over the duration at which the target disappeared, the robot correctly predicted his/her trajectory, planned the trajectory to recover the target, and resumed tracking the target (Figure 9d). The robot continued to follow the target and detected another person who stood up in the corridor as a non-target (Figure 9e). The target walked in an approximately straight line for a few meters, then turned right 90\({}^{\circ}\) at the second corner (Figure 9f). The target suddenly disappeared again from the FoV of the camera (Figure 9g). The robot correctly predicted the target's trajectory and planned the recovery of the target to the tracking state at the destination position where the target arrived (Figure 9h). In this experiment, the mobile robot achieved continuous success in following the target person despite two sudden disappearances. To assess the performance of the proposed system, we set two criteria for the success of the mission. The first criterion was whether the robot successfully followed the target person to the destination point. If the robot did not reach the destination point, we considered the experiment a failure, regardless of the causes of the failure or the percentage of the travel distance during the experiment. The second criterion was whether the robot correctly predicted the target's future path trajectory after missing the target at the corners. In all the experiments, the velocities, coordinates, distances, and time were measured in meters per second, meters, meters, and seconds, respectively. The results of the 10 experiments are summarized in Table 1. In the experiments, the robot followed the person toward the destination successfully in nine out of ten attempts and predicted the correct direction at both corners. The proposed system failed in the eighth mission because it misidentified another person as the target. The traveled distance was 14.2 m when the failure occurred while the target person was walking towards the destination location. Because of this failure, the robot did not predict the target's trajectory  
Figure 8: Realistic scenario of the target and robot in the environment. at the second corner, although it had correctly predicted the direction of the target at the first corner. The average and the total traveled distance, time, and frames of the experiments were (21.4 m, 47.6 s, and 1157 frames) and (214.3 m, 475.5 s, and 11,571 frames), respectively.