## 4 Results and Discussion
### Comparison with Previous Approaches  
To evaluate the proposed approach in this paper, its performance was compared with our previous method [7] and another method closely related to our work [21].  
The main improvements of this work compared to our previous work were a faster recovery time and a much higher recovery success rate when the robot lost the target. Since the previous work was designed to recover the target basically by a random search, it would take a much longer time to recover or never recover. As explained in the method section, we improved the state transition control including recovery by adapting online trajectory prediction based on the past target trajectory.  
To compare the proposed work with our previous work in uniform operating conditions, we conducted 10 experiments in the same environment (path) with the same system that was used in our previous work, as shown in Table 2. All performance measures in this table are the same as in Table 1. The path included only one corner. The target person walked out of the laboratory and then turned right to continue walking to the end of the corridor, as shown in Figure 10 in our previous work [7]. The average recovery time of the proposed system was 4.13 s, while it was 15.6 s (30.7-15.1 s) for the same corner, as shown in Figure 11 in our previous work. It was about 3.7 times faster than our previous work. Meanwhile, the average successful tracking rate increased from 0.58% (see Table 2 in our previous work) to 0.84% (see Table 2). The main idea behind the online trajectory prediction was to obtain the fastest recovery time of the missing target to the tracking state using the LLP state. In the previous experiments, two videos were recorded by the robot system ([[https://www.youtube.com/watch?v=V59](https://www.youtube.com/watch?v=V59) DDQz912k]([https://www.youtube.com/watch?v=V59](https://www.youtube.com/watch?v=V59) DDQz912k) (accessed on 13 April 2021)) and a smartphone ([[https://www.youtube.com/watch?v=2](https://www.youtube.com/watch?v=2) VwaRBeYglc]([https://www.youtube.com/watch?v=2](https://www.youtube.com/watch?v=2) VwaRBeYglc) (accessed on 13 April 2021)).  
The comparison with another related approach for the overall system was difficult due to several factors such as non-identical operating conditions, unavailability of a common dataset, different sensors/hardware, environmental geometry, and so on [9]. However, we tried our best to compare our system with the previous work [21] closely related to our work at an individual module level rather than the full system. We implemented two main algorithms and compared them with ours. [PERSON] et al. [21] employed the YOLOv2 algorithm for tracking the people and the VBLR model to predict the target's trajectory. The proposed system adopted the SSD algorithm for tracking the people and the LR model to predict the target's trajectory. YOLOv2 achieved real-time performance at 2.50 fps, while the SSD algorithm achieved 25.03 fps using the CPU. The SSD was about 10 times faster than YOLOv2; thus, the robot movements will be more responsive to ambient changes and more aware of the target's movements. The frame rate was still within the given specification limits of the camera used. For trajectory prediction models, Figure 5 shows the comparison between the LR and VBLR models.