# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
## 2 Related Work  
### Siamese Network-Based Trackers  
In recent years, Siamese network-based tracking algorithms have treated tracking as a metric problem. They have attracted much attention for their excellent trade-offs in terms of speed and accuracy. [PERSON] et al. [11] first introduced SiamFC for visual tracking, aiming to learn the similarity metric between target templates and search regions and localize objects using correlation operations. With the success of RPN on object detection, [PERSON] et al. applied RPN to the Siamese Network framework to solve the multi-scale problem of objects. Although the SiamRPN network has improved performance in tracking, it cannot use the deeper backbone network and still uses the external AlexNet network [20]. To use the powerful depth features extracted by deep networks, SiamRPN++ and SiamDW address the challenges of introducing deep networks into the Siamese framework, resulting in a significant performance improvement. [PERSON] et al. introduced the hard negative mining technique in DaSiamRPN [15] to overcome the data imbalance issue by including more semantic negative pairs into the training process. In order to solve the shortcomings of the algorithms for target representation and running speed, SiamMask [21] is used as an integrated processing framework for visual object tracking and video object segmentation tasks.  
The Siamese-based tracker does not perform any model updates or a simple linear update strategy, so the performance completely depends on the general matching ability of the Siamese network. However, the appearance of objects in the presence of tracking often varies greatly, and failure to update the model causes the tracker to fail. [PERSON] et al. proposed the DSiam [22] tracker and designed dynamic transformation matrices. [PERSON] et al. proposed the MemTrack [23] that dynamically writes and reads previous templates to cope with target appearance variations. And [PERSON] et al. proposed the UpdateNet [24] which learns a generic function that computes the updated template based on an initial ground-truth template, accumulated template, and the predicted template in the current frame.  
Figure 1: Drift problem leads to wrong target, when there is similar object interference in complex scenes. Green box is ground true and yellow box is the result of a Siamese-based tracker.  
Recently,There have been several notable transformer trackers, such as TransT [25], DualITFR [26], and STARK [27], which introduce the transformer to tracking framework for stronger information interaction and achieve compelling results. SiamBAN [28] and SiamCAR [29] employed full convolutional networks to directly classify objects and regress their bounding boxes at each spatial position, removing the problematic hyperparameter adjustments of anchor points. Although the performance of the existing Siamese network tracking algorithms has improved significantly, the feature representation capability of the backbone network is still lacking, and the ability to discriminate similarity interferers is not robust enough.