## 4 Results  
In this section, we used the original KCF, four the state of the art tracking algorithms proposed recently, and our adaptive dynamic multi-template object tracker to perform the tracking experiments. In the experiments, all objects suffered the problems of motion blur, deformation, illumination variation, and occlusions. We used four scenes to represent the objects encountering motion blur, deformation, illumination variation, and occlusion, respectively. Firstly, we used the first scene to experiment with the effect of various trackers on the tracking of objects in a light-changing environment, as shown in Figure 9. Secondly, we used the second scene to experiment with the tracking effect of various trackers for tracking the deformation object, as shown in Figure 10. Thirdly, we used the third scene to experiment with the tracking effect of various trackers for tracking the moving blur object, as shown in Figure 11. Finally, we used the fourth scene to  
Figure 11: The experimental results of the scenario 3 video with motion blur: (**a**) the 1 st frame of the video; (**b**) the 15 th frame of the video; (**c**) the 31 st frame of the video; (**d**) the 46 th frame of the video; (**e**) the cropped and enlarged image of image (**a**); (**f**) the cropped and enlarged image of image (**b**); (**g**) the cropped and enlarged image of image (**c**); (**h**) the cropped and enlarged image of image (**d**). Figure 12: The experimental results of the scenario 3 video with occlusion: (**a**) the 100 th frame of the video; (**b**) the 113 rd frame of the video; (**c**) the 140 th frame of the video; (**d**) the 172 nd frame of the video; (**e**) the cropped and enlarged image of image (**a**); (**f**) the cropped and enlarged image of image (**b**); (**g**) the cropped and enlarged image of image (**c**); (**h**) the cropped and enlarged image of image (**d**). The algorithms we used as the experimental control group were KCF, MCCTH, MKC-Fup, LDES, and SiamRPN++ [36, 37, 38, 39, 40]. According to [PERSON] et al., the KCF using HOG features for object tracking, has the advantage of high tracking accuracy rather than using grayscale features or color features [36]. According to [PERSON] et al., MCCTH uses an adaptively updated multi-cue analysis framework for object tracking and has good object tracking robustness [37]. According to [PERSON] et al., MKCFup introduces a new type of multi-kernel learning (MKL), which can use the powerful discriminability of non-linear kernels to track objects and can track high-speed moving objects [38]. According to [PERSON] et al., LDES can overcome the scale change, rotation and large displacement of the target when tracking the target [39]. [PERSON] et al., used a training dataset that locates objects biasing from the image center to deepen the network of SiamRPN++ and proposed a new model architecture to perform layer-wise and depth-wise aggregations to improve tracking accuracy [40]. Tracking experiments were performed for scenarios 1, 2, 3, and 4. Some screenshots of the tracking results using the algorithms ADMTCF, KCF, MCCTH, MKCFP, LDES, and SIAMPRN++ are shown in Figures 9-12, respectively. The experimental data are shown in Tables 1 and 2. In scenario 1, the target is a red car driving out of the tunnel from the tunnel. In the tunnel, due to the light, the target appears dark red nearly black. When the target exits the tunnel and encounters light outside the tunnel, it has a pristine red appearance. This video tests the tracker's ability to track the color of part or all of an object's appearance as the illumination varies. According to Table 1, the experimental results show that the algorithms KCF, MKCFup, LDES and our tracker showed good tracking ability in the case of illumination variations. In scenario 2, the target is a fish, which moves from the left to the right. During the movement of the fish, the fish gradually turned from sideways to front facing the camera. The image features, size, and outline of the fish's side and front are different.