# Faster MDNet for Visual Object Tracking
## 1 Introduction
Based on DiMP, PrDiMP [16] uses a probabilistic regression-based approach to model the tracker and scale prediction modules, which alleviates the interference of imprecise or ambiguous noise labels on the network and effectively improves tracking accuracy. Due to the use of complicated deep learning network structures, these methods typically require a large storage space. Conventional trackers generally treat the tracking task as frame-by-frame object detection while disregarding the abundant temporal relations. [PERSON] et al. [17] explored temporal information in videos by utilizing the Transformer structure to bridge independent video frames. TransT [18] draws on the Transformer structure for improving feature fusion operations in traditional two-way networks. TransT also employs the attention mechanism from Transformer to fuse the template information into the search region for better object localization and scale regression. SwinTrack [19] allows full interaction between the object and the search region, as well as a comprehensive study of different strategies for feature fusion, location encoding, and training loss to further improve model performance. These approaches employ deep CNN models, such as ResNet 50 [12]. Sophisticated models consume tens or even hundreds of megabytes of memory, limiting their practical application. MDNet [20] is the first lightweight tracker to introduce classification-based networks into the field of tracking. To address the problem that a target in one video may become part of the background in other videos, a training mode under multiple target domains is introduced. VITAL [21] implements a generative adversarial network in MDNet to interfere with classifiers by generating masks with occlusion properties, making the features learned by classifiers more robust. RT-MDNet [22] introduces ROI Align [23] to MDNet, which improves the tracking speed. However, the large number of features to be extracted limits the tracking speed of these trackers. Based on these findings, we propose a novel network structure, Faster MDNet, to seek a balance among tracking accuracy, speed, and computational resource consumption. First, we introduce a channel attention module after convolutional layers to implement a strategy for capturing cross-channel interactions using fast one-dimensional convolution. Second, redundant information is effectively removed, and more generic features are transferred to our model by domain adaptation components. Last, we design an adaptive spatial pyramid pooling layer. Since feature maps of different sizes are input to this layer and will become the same size, this layer can be utilized as a substitute for fully connected layers. Our contributions are summarized as follows:* We introduce a channel attention module after convolutional layers. Model robustness is improved by fast one-dimensional convolution without dimensionality reduction while adding only a few parameters. We use the ILSVRC dataset from the image classification domain as training data and design domain adaptation components for transferring feature information. The adaptive spatial pyramid pooling layer with a multi-scale feature fusion strategy is implemented to reduce model complexity and to improve model robustness. * Our model has a simple structure that enables lightweight tracking. Due to a significant reduction in model complexity, the proposed tracker has a high real-time speed. The tracker uses both high-level semantic information and low-level semantic information for a higher tracking accuracy. The experimental results obtained with several popular datasets demonstrate that our approach has a performance similar to that of current state-of-the-art trackers while consuming fewer computational resources. We note that a shorter conference version of this paper appeared in Progress in Informatics and Computing (2021). The proposed method in our initial conference paper did not achieve a tracking accuracy comparable to that of state-of-the-art trackers.