## 5 Experiments
Figure 6 presents the success plots on 9 challenging scenarios: fast motion (FM), motion blur (MB), deformation (DEF), illumination variation (IV), in-plane rotation (IPR), low resolution (LR), occlusion (OCC), out-of-plane rotation (OPR) and scale variation (SV). Our proposed tracker performs best in most scenarios. We can see that the success rates in the scenarios of background clutter, motion blur, deformation and illumination variation in (b), (c), and (d), respectively, are approximately twice those of the original correlation filter that is obtained by combining colour attributes. In (i), our approach relizes a larger improvement by approximately 14.2% compared to Staple, which does not utilize adaptive scale estimation. In (g), it demonstrates strong performance, which is due to the effect of the high-confidence judgement mechanism when encountering update problems. According to these experiments, our proposed fusional multi-correlation -filters with the high-confidence judgement mechanism can outperform state-of-the-art trackers in most scenarios. Figure 5: The visualization of comparison results of our proposed tracker and other existing trackers on five challenging sequences, i.e, (**a**) Skating, (**b**) Couple, (**c**) Subway, (**d**) Jogging-2, (**e**) Liquor. ### Experiment with the Target Tracking Robot

Target tracking robots are potential applications of human-robot collaboration and can be applied to service areas, such as industrial plants, offices, supermarkets, airports, etc. and collaboratively provide information, guidance and/or physical assistance to people. The main challenge for the target tracking robot is to track the user accurately and in real-time. The robot platform used in this experiment was a GQY robot. GQY robot is a comprehensive experimental platform, which is equipped with a monocular camera, lidar, ultrasonic sensor, and other sensors. We used the GQY robot to experiment with our tracker. If the robot can not track the experimenter in real-time, it will not follow the experimenter in an S-shaped path. Figure 7 shows the target tracking robot following the experimenter in an S-shaped path. We repeated the experiment more than 20 times. Thanks to the real-time improvement of our tracker, the target tracking robot can track the target stably in real-time. As shown in Figure 8, the illumination variation during the process is very obvious. Our tracker, which incorporates colour attributes, is more robust in scenes with illumination variation, and illumination variation does not affect the accuracy and stability of robot tracking. In a variety of challenging environments, the target tracking robot can still perform effective and accurate tracking, which illustrates the effectiveness of our tracker and can meet the needs of the target tracking for real-time target tracking. Figure 6: The success plots of OPE on 9 challenging circumstances. ## 6 Conclusions

In this paper, a novel tracker is proposed for overcoming challenges such as deformation, scale variation, and occlusion in the field of visual tracking. The colour attributes are combined, and due to their complementary characteristics, they are used to well handle variations in shape. Correlation-filter-based trackers have theoretical limitations. For example, large shape changes can lead to more background be learned, which affects the correlation-filter-based trackers. To mitigate the problems that are caused by background clutters, fast motion and scale variations, an innovative scale estimation filter is utilized. Furthermore, a high-confidence mechanism is proposed for preventing model corruption. This tracker not only performs excellently but also satisfies the real-time performance requirement of online object tracking. Deep learning has advantages in feature representation. The trained network can achieve high accuracy.