# Faster MDNet for Visual Object Tracking
## 1 Introduction  
As one of the fundamental tasks in computer vision, object tracking methods use the contextual information of video sequences to model object appearance and motion information for predicting object positions and motion states. Civil and military systems based on video object tracking have landed and are widely employed in important fields, such as intelligent transportation, autonomous driving, unmanned aerial vehicles, human-machine interaction, and radar tracking. Since AlexNet [1] made a large splash in image classification tasks, deep learning has gained widespread attention. With a powerful feature extraction capability and end-to-end training models, deep learning techniques have made great progress in computer vision, machine learning, natural language processing, and other fields. In the past few years, deep learning-based object tracking algorithms have made significant breakthroughs. The initial deep tracking methods focused on correlation filters, which replace manual features in traditional correlation filters with deep features or combine end-to-end training of deep networks with correlation filters. HCF [2] is proposed to separately train correlation filters for deep features of different layers and perform feature fusion to better utilize the deep model. Subsequently, HDT [3] adaptively changes the weights of filters at different scales, and MCCT [4] combines various features of filters and switches them adaptively. Based on deep correlation filter tracking methods, C-COT [5] focused on the problem of response map fusion caused by differences in the resolution of deep features at different scales. Inspired by C-COT, ECO [6] performs adaptive correlation filter selection, clustering of target samples, and sparse target updating to obtain further optimization in terms of efficiency and storage. Despite the excellent performance achieved by the above-mentioned deep correlation tracking methods on several datasets, the time-consuming extraction of features keeps them from achieving real-time speed even when running on an advanced GPU. In 2016, [PERSON] et al. proposed the Siamese network SiamFC [7], which extracts features from the target template and search region by using a convolutional network and then generates the response map. The peak point on the response map is the target position. Considering that the regression of the target scale by SiamFC in a traditional scaling form still cannot accurately obtain scale information of the target, [PERSON] et al. presented SiamRPN [8]. This method introduces the RPN [9] structure from object detection to SiamFC. Features are extracted by using the shared parameters. The location of the target is obtained through a classification branch, and an accurate estimate of the target scale is obtained by the regression branch. [PERSON] et al. further proposed DaSiamRPN [10], which improves the anti-interference ability of the tracker by effectively using negative samples. Nevertheless, AlexNet [1] is employed as the feature extraction network in the abovementioned methods instead of the more robust models that are currently utilized. SiamRPN++ [11] modifies the ResNet [12] network to obtain promising results on several datasets. Ocean [13] used an anchor-free regression network based on SiamFC [7] to regress the object region over a large space. Despite the great success of the abovementioned approaches, the lack of an online update process for these methods causes the problem that trackers cannot automatically adjust parameters to suit changes in objects. ATOM [14] uses a conjugate gradient strategy combined with a deep learning framework for jointly fast optimization. The authors further employed the idea in DiMP [15] for end-to-end learning to achieve leading performance by learning all parameters through neural networks.