## 2 Related Work
Many scholars have proposed methods trying to solve the object encountered illumination changes in the tracking. In 2017, [PERSON] et al. proposed foreground detection in visible and thermal images to reduce the effects of red-green-blue (RGB) color on lighting noise and shadows to reduce the impact of illumination variations on tracking moving objects [24]. In 2018, [PERSON] et al. proposed an optimized discriminative correlation filter (DCF) tracker that improves the accuracy under illumination changes by performing multiple region detection and using alternate templates (MRAT) while saving alternate templates through a template update mechanism [25]. In 2021, [PERSON] et al. proposed a neural network that uses the historical location of the target combined with the historical location of the target to expand the training data and uses the metric loss of the historical appearance feature of the target to train the appearance feature extraction module to improve the extraction performance to address the effect of lighting changes on tracking moving objects [26]. In 2022, [PERSON], [PERSON], and [PERSON] proposed SiamET, a Siamese-based network using Resnet-50 as its backbone with enhanced template modules. They address the effect of illumination variations on tracking moving objects by using templates that are obtained based on all historical frames [27]. Many scholars have proposed methods trying to solve the object encountered occlusion changes in the tracking. In 2019, [PERSON] et al. proposed a new dynamic saliency-aware regularized CF tracking (DSAR-CF) scheme that defines a simple and efficient energy function to guide the online update of the regularized weight map to address the effect of occlusion when tracking [28]. In 2020, [PERSON] et al. proposed a scale-adaptive object-tracking method to reduce the impact of occlusion on tracking moving objects. They extracted features from different layers of ResNet to produce response maps fused based on the AdaBoost algorithm, prevented the filters from updating when occlusion occurs, and used a scale filter to estimate the target scale [29]. In 2020, [PERSON] et al. designed a mask set to generate local filters to capture the local structures of the target and adopted an adaptive weighting fusion strategy for these local filters to adapt to the changes in the target appearance, which could enhance the robustness of the tracker effectively [30]. In 2021, [PERSON] et al. constructed the subspace with image patches of the search window in previous frames. When the appearance of an object is occluded, the original image patch used to learn the filter is replaced by the reconstructed patch so that the filter can learn from the object instead of the background, thus reducing the effect of occlusion [31]. In 2022, [PERSON] et al. demonstrated the effect of a simple motion model, observation-centric SORT (OC-SORT), to reduce the errors accumulated by linear motion models during loss, so that they could reduce the effect of occlusion phenomena on tracking moving objects [32]. However, most of these scholars only propose individual solutions to the problems of deformation, illumination variation, motion blur, and occlusion when tracking moving objects. One solution for a single phenomenon is not enough to track moving objects correctly and stably. When the surface features of the tracked object are drastically changed due to the simultaneous occurrence of various factors such as deformation, illumination variation, motion blur, and occlusion, the tracker that can only solve a single phenomenon will still result in low tracking accuracy or tracking loss. Unfortunately, when tracking moving objects, four phenomena, such as deformation, illumination variation, motion blur, and occlusion, are encountered almost simultaneously. Because of the above reasons, we propose an adaptive dynamic multi-template object tracker (ADMTCF) in this paper, which can simultaneously overcome the difficulty of tracking moving objects with deformation, motion blur, illumination variation, and occlusion.