Recently, correlation filter-based tracking methods have been widely adopted in UAV target tracking due to their outstanding performance and excellent tracking efficiency. However, existing correlation filter-based tracking methods still face issues such as redundant visual features with weak discriminative ability, inadequate spatio-temporal information mining, and filter degradation. In order to overcome these challenges, this paper proposes a spatial saliency-aware strategy that reduces redundant information in spatial and channel dimensions, thus improving the discriminative ability between the target and background. Also, this paper proposes a position estimation mechanism under spatio-temporal joint constraints to fully mine spatio-temporal information and enhance the robustness of the model in complex scenarios. Furthermore, this paper establishes a positive expert group using historical positive samples to assess the reliability of candidate samples, thereby effectively mitigating the filter degradation issue. Ultimately, the effectiveness of the proposed method is demonstrated through the evaluation of multiple public datasets. The experimental results reveal that this method outperforms others in tracking performance under various challenging conditions.

In recent years, Transformer-based object trackers have demonstrated exceptional performance in object tracking. However, traditional methods often employ single-scale pixel-level attention mechanisms to compute the correlation between templates and search regions, disrupting object's integrity and positional information. To address these issues, we introduce a cyclic-shift mechanism to expand the diversity of sample positions and replace the traditional single-scale pixel-level attention mechanism with a multi-scale window-level attention mechanism. This approach not only preserves the object's integrity but also enriches the diversity of samples. Nevertheless, the introduced cyclic-shift operation heavily burdens storage and computation. To this end, we treat the attention computation of shifted and static windows in the spatial domain as convolution. By leveraging the convolution theorem, we transform the attention computation of cyclic shift samples from the spatial domain to element-wise multiplication in the frequency domain. This approach enhances computational efficiency and reduces data storage requirements. We conducted extensive experiments on the proposed module. The results demonstrate that the proposed module outperforms multiple existing tracking algorithms regarding performance. Moreover, ablation studies show that the method effectively reduces the storage and computational burden without compromising performance.

The correlation filter object tracking algorithm has gained extensive attention from scholars in the field of tracking because of its excellent tracking performance and efficiency. However, the mathematical modeling relationships of correlation filter tracking frameworks are unclear. Therefore, many forms of correlation filters are susceptible to confusion and misuse. To solve these problems, we attempted to review various forms of the correlation filter and discussed their intrinsic connections. First, we reviewed the basic definitions of the circulant matrix, convolution, and correlation operations. Then, the relationship among the three operations was discussed. Considering this, four mathematical modeling forms of correlation filter object tracking from the literature were listed, and the equivalence of the four modeling forms was theoretically proven. Then, the fast solution of the correlation filter was discussed from the perspective of the diagonalization property of the circulant matrix and the convolution theorem. In addition, we delved into the difference between the one-dimensional and two-dimensional correlation filter responses as well as the reasons for their generation. Numerical experiments were conducted to verify the proposed perspectives. The results showed that the filters calculated based on the diagonalization property and the convolution property of the cyclic matrix were completely equivalent. The experimental code of this paper is available at https://github.com/110500617/Correlation-filter/tree/main.