## 5 Conclusions  
To effectively address the problem of tracking performance degradation and tracking failure of single-object tracking algorithms based on Siamese full convolutional networks in complex scenarios, we propose a single-object tracking algorithm for Siamese networks that incorporates multiple attention mechanisms and adaptive updating strategies for background features. To this end, we make four contributions to the algorithm: (1) We designed a backbone feature extraction network using a small convolutional kernel to fuse jump-layer connected features, which can not only effectively extract the deep semantic features of the object, but also fuse the mid-level features with the deep features to enhance the model's characterization ability. Meanwhile, using a small convolutional kernel for convolution not only reduces the parameters of the network, but also increases the depth of the network while ensuring computational speed. (2) We also propose an adaptive updating strategy for background features and apply it to the processing of object templates, i.e., the linear combination of object features obtained from a processed object template with those obtained from another unprocessed object template through a backbone feature extraction network and multiple attention mechanism can improve the model's ability to distinguish between object and background features. (3) We also integrated the multiple attention mechanism in the algorithm, which can make the algorithm learn to pay attention to the channel, space, coordinates, and other feature information, and enhance the feature expression ability, attention ability, and precision of the entire algorithm. (4) We convolve the original object features with the original search region features in our algorithm, and at the same time convolve the object features after the attention mechanism with the search region features after the attention mechanism, and linearly fuse the interrelated results of the two convolutions to obtain the final response map to enrich the response of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tracking Algorithms** & **EAO** & **Precision** & **Robustness** \\ \hline Our & 0.229 & 0.511 & 0.514 \\ MEEM & 0.192 & 0.463 & 0.534 \\ siamFC & 0.188 & 0.503 & 0.585 \\ DCFNet & 0.182 & 0.470 & 0.543 \\ SRDCF & 0.119 & 0.490 & 0.974 \\ DSST & 0.079 & 0.395 & 1.452 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the results of this algorithm with other algorithms on the VOT2018 dataset.  
We validate the performance of our algorithm on the object tracking benchmark datasets OTB and VOT and compare it with other algorithms. The validation and comparison results show that our algorithm can effectively solve the problem of object tracking in complex scenes and further improve tracking precision and precision while ensuring tracking speed.  
Conceptualization, W.F. and F.M.; methodology, W.F. and C.Y.; software, W.F. and A.Y.; validation, W.F., F.M. and C.Y.; formal analysis, F.M.; investigation, C.Y.; resources, A.Y.; data curation, W.F.; writing--original draft preparation, W.F.; writing--review and editing, W.F.; visualization, W.F.; supervision, F.M.; project administration, F.M.; funding acquisition, F.M. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the China Academy of Engineering Physics under Grant No. 61426050303-2.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank the Institute of Applied Electronics, China Academy of Engineering Physics for supporting this research project.  
The authors declare no conflicts of interest. The funders had no role in the study's design; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.