## 2 Related Work
We believe a great tracker must have the following capabilities to simultaneously overcome the four problems of motion blur, deformation, illumination variation, and occlusion when tracking. * The template of the tracking object must have sufficient characteristics of the target object;
* The template of the tracking object must not be sensitive to illumination variations;* The template of the tracking object must be more than one set;
* The template of the tracking object must be dynamically updatable. [PERSON] and [PERSON] proposed similar concepts. They believed that using multiple and multi-scale templates can improve tracking accuracy and can overcome the shortcomings of KCF's single template size [33]. We differ from them in two ways. The first difference is that they use multiple templates to calculate at the same time, but we chose the template with the highest similarity for tracking the next frame of the target after calculations and sorting of multiple templates. The second point is that they update the scale of the tracker by maximizing over the posterior distribution of a grid of scales, but we use the most similar template and then adjust the size of the template adaptively to further improve the similarity between the template and the target. The adaptive dynamic multi-template object tracker we proposed in this paper has several characteristics. First, we convert the image of the selected object from RGB to HSV color space and then perform LBP conversion on the luminance to obtain a sample of the object's image feature [34; 35]. After HSV color space conversion and local binary pattern (LBP) conversion, the image features do not change drastically with the illumination variation. Therefore, our tracker does not reduce the stability and accuracy of image tracking due to changes in ambient light. Secondly, different from the general tracker, the tracker we proposed not only has multiple sets of samples sorted by time but also dynamically updates or adds the tracker templates when tracking objects. Our strategy for updating or adding new tracker templates must meet one of two conditions. The first condition is to choose a smaller change threshold when the characteristics of the target change greatly. The second condition is to choose a larger change threshold when the characteristics of the target change less. Since our tracker has multiple sets of templates sorted by time and can be updated dynamically, it can overcome the tracking loss caused by deformation or motion blur during tracking. Third, our tracker template can resize as the surface features of moving objects change. To compare with our adaptively adjusting template, the traditional use of a single threshold to judge the similarity between the moving object and the template is too monotonous and prone to tracking loss. Therefore, during the tracking process, our tracker can exhibit excellent tracking robustness even if the target is occluded. In this paper, we have two main contributions. The first contribution is that our proposed ADMTCF algorithm can maintain the tracking accuracy and robustness even if the object encounters deformation, illumination change, motion blur, and occlusion. The second contribution is that we propose an evaluation method with a penalty factor, which can objectively reflect the accuracy of various algorithms for estimating the object's size.