# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 3 UAV Localization and Tracking
### UAV Tracking
#### 3.2.2 Deep Siamese Networks  
Deep Siamese Networks (SNs) have become a popular approach for learning similarities between target images and search image regions in visual tracking tasks. With SNs, real-time applications can fully leverage end-to-end learning and overcome the limitations of pretrained CNN. Siamese trackers are instructed to deal with various tracking challenges, such as rotations, perspective changes, and lighting changes, by using offline training videos. This enables the tracker to accurately localize target objects in consecutive frames [102]. Over the years, several advanced Siamese-based trackers have been proposed, such as SiamFC (Fully-Convolutional Siamese Networks) [109], SiamRPN++ (Siamese region proposal network plus plus) [110], and LTMU (High-Performance Long-Term Tracking with Meta-Updater) [111]. The robustness of these trackers against challenging scenarios, combined with their state-of-the-art performance on several benchmark datasets, has established them as highly competitive methods in the area of UAV tracking. SiamFC [109]  
A fully convolutional Siamese network is used in this classic tracking algorithm to perform cross-correlations between the template slice and the search area in order to locate the target. Additionally, a multi-scale strategy is employed to determine the appropriate scale of the object being tracked. By leveraging the discriminative information learned from the offline training videos, the Siamese-based tracker can accurately estimate the position and scale of the target object in real-time. On several benchmark datasets, this method has shown to achieve state-of-the-art performance and has gained considerable attention in the UAV tracking field. SiamRPN++ [110]  
SiamRPN++ is an advanced tracking algorithm based on the Siamese architecture that incorporates a region proposal network (RPN) into the Siamese network, enabling a very deep backbone network. The overall framework consists of two branches: a classification branch, which selects the optimal anchor, and a regression branch, which predicts the offsets of the anchor. By leveraging the RPN mechanism, SiamRPN++ can generate high-quality proposals for the target object, which enhances the robustness and speed of the tracking algorithm. SiamRPN++ is more robust and faster than SiamFC, thanks to the introduction of the RPN mechanism and the removal of the multi-scale strategy. SiamRPN++ has demonstrated state-of-the-art performance on several benchmark datasets and has become one of the most competitive tracking methods in the field of UAV tracking. LTMU [111]  
This method is not only a short-term tracker but also a long-term tracker. The main contribution of this method is its offline-trained meta-updater, which is utilized to determine whether the tracker needs to be updated in the current frame or not, resulting in improved robustness. Furthermore, a long-term tracking framework is designed that utilizes a SiamRPN-based re-detector, an online verifier, and an online local tracker in conjunction with the proposed meta-updater. By integrating these components, the tracker can maintain accurate tracking of the target object over an extended period, even in scenarios where the object undergoes significant changes, such as occlusion or appearance variations. In both short- and long-term tracking benchmarks, this method has demonstrated excellent discriminative capability and robustness, making it an extremely competitive method. While the Kalman filter is widely used in UAV tracking tasks due to its low computational cost, achieving high accuracy results in practical applications is often challenging [112]. Recently, DCF methods and Siamese tracking algorithms have shown excellent performance in object tracking, thanks to their use of end-to-end offline learning. However, this has only been possible with the availability of large-scale training datasets [102]. Despite these advancements, achieving low-cost, high-precision real-time UAV tracking remains a challenge in anti-UAV research.