# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
###### Abstract  
The target tracking of unmanned aerial vehicles (UAVs) has attracted significant attention recently with the increasing application of UAVs, yet few studies have made breakthroughs in dynamic dim target detection. How to efficiently and accurately identify dynamic dim targets in complex contexts poses a challenge. To address this issue, we proposed an improved lightweight Siamese network (ILSN) with an optimized feature-extraction network design and similarity measurement.As for the feature-extraction network, we built a position-wise attention module to obtain the target feature's position information, which enhanced the network's ability to extract weak targets while reducing the model parameters, thus ensuring the network is lightweight. For the similarity-measurement module, the tracking accuracy was expected to be improved by deeply mining the localization information of the shallow features and the semantic information of the deep features in the feature networks. To evaluate the performance of the proposed method, we established a simulated experimental environment and a physical experimental platform and then carried out comparison experiments on the attention modules, tracking accuracy, and efficiency performance. The experimental results showed that, compared with the five introduced comparison algorithms, the ILSN had apparent advantages in tracking accuracy: the tracking speed reached 108 frames per second, which met the real-time requirements while improving the success rate. umanned aerial vehicle (UAV); dim target tracking; deep learning; feature extraction +
Footnote †: journal: Applied  
occlusion severely affects target tracking accuracy; continuous occlusion may cause target loss [5]. * Similar interference: there is a huge challenge in the accurate and robust tracking performance of UAVs under a similar target interference in conditions where the tracking target features are not obvious or the features are approximated [6]. * Target distortion: due to factors such as rapid target motion, background interference, and scale changes, target distortion often exists in UAV recognition targets, which affects feature recognition and judgment. The authors of [7] emphasized the complexity and uncertainty of the tracking scenarios of UAVs, which usually encounter significant environmental disturbances during flight. This image distortion will reduce the target characteristics. * Illumination changes: there are complex illumination changes in UAV target tracking that mainly include dynamic changes in five texture-feature parameters such as energy, entropy, correlation, contrast, and local uniformity, which then lead to poor-quality image recognition [8]. Different target tracking methods have been analyzed in UAVs that cover background denoising, target enhancement, optimum control, and intelligent identification. [PERSON] et al. [9] stated that they believe that detection measures such as the mean filter, Gaussian filter, median filter, and bilateral filter are inefficient for fast and robust identification. The authors of [10] and [11] studied the target tracking process and showed that Siamese networks had an excellent target tracking performance and have tremendous potential for application. Although researchers have conducted many studies, research on the dynamic dim target tracking of UAVs is still in its infancy. In addition, the increases in complex operating scenarios and target usage will result in more challenges. Considering these current issues, we provide a detailed overview of the recent works and a comparative analysis of related works. Motivated by the drawbacks of the current work, we focus on the fast and robust tracking of targets, propose a dynamic dim target tracking approach for UAVs using an improved lightweight Siamese network (ILSN), and carry out a performance test that was conducted on a physical experimental platform.