# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 1 Introduction  
Object tracking is one of the research hotspots in the field of computer vision, and its primary task is to specify a target object in the initial frame of a video and continuously track this object with a rectangular box in subsequent video frames to achieve target localization and scale estimation [1, 2]. It finds extensive applications in various domains, such as public safety [3, 4], autonomous driving [5, 6], image processing [7], and sports competitions, etc. [8]. Despite the success achieved by existing tracking algorithms on simple datasets like OTB [9], the actual trackers are often disturbed or simultaneously affected by factors such as Fast Motion, Illumination Variation, and Scale Variation. The increasing video resolution also imposes specific requirements on the speed of the tracking algorithm, hindering their practical applications in real-world scenarios [10, 11]. Therefore, proposing an algorithm capable of achieving real-time object tracking in large datasets containing various complex environments is of paramount significance [12]. Siamese tracking algorithms utilize dual-branch convolutional neural networks with shared weights to extract target object features, calculate the similarity between the target region and the search region, and determine the tracking target object's position. Scholars have attempted to enhance tracking accuracy and success rate by incorporating Transformers to fuse deep features or extract global features [13, 14]. However, existing Siamese tracking algorithms still face limitations in tracking capability under complex environments, primarily due to:1. Traditional Siamese tracking algorithms use convolutional neural networks or Transformer as the backbone, allowing the utilization of features from a global perspective, enhancing the ability to learn long-range feature representations. However, due to the lack of utilization of local detailed information, this type of tracking algorithm is not robust when faced with interference from complex environments such as Fast Motion [15; 16]. 2. As a large visual model, Siamese tracking algorithms' self-attention computation is complex, leading to poor real-time tracking performance [17]. Spurred by the above deficiencies, in order to implement a robust and real-time tracking algorithm, we propose a transformer tracker called SSTrack based on spatial scale attention. Figure 1 illustrates the comparison results of our tracking algorithm, SSTrack, with other mainstream object tracking algorithms. SSTrack achieved 71.5% AO on the GOT-10K and 68.4% AUC on the LaSOT, taking the lead. The main contributions of this paper are as follows:  
* Addressing issue 1, we employ the Swin Transformer as the backbone and construct a spatial scale attention module. This module aims to perceive spatial and multi-scale information in the feature sequence while extracting global features. * Addressing issue 2, we adjust the utilization of position encoding in the feature fusion stage and the computation method of self-attention. This adjustment avoids the need to retrain the Swin Transformer on ImageNet. It reduces the complexity of self-attention computation, thus improving tracking speed and meeting real-time tracking requirements. * To validate the proposed algorithm's effectiveness, we compare SSTrack with mainstream tracking algorithms on large tracking datasets such as GOT-10k, TrackingNet, and LaSOT. The test set covers various tracking scenarios, including indoor and outdoor, and target categories, such as pedestrians and vehicles. Additionally, ablation experiments are conducted. The results demonstrate that compared to other mainstream Siamese tracking algorithms, the proposed algorithm achieves high tracking accuracy, maintains robustness in complex tracking scenarios, and satisfies the real-time tracking requirements.