Although single object trackers have achieved advanced performance, their large-scale models make it difficult to apply them on the platforms with limited resources. Moreover, existing lightweight trackers only achieve balance between 2-3 points in terms of parameters, performance, Flops and FPS. To achieve the optimal balance among these points, this paper propose a lightweight full-convolutional Siamese tracker called LightFC. LightFC employs a novel efficient cross-correlation module (ECM) and a novel efficient rep-center head (ERH) to enhance the nonlinear expressiveness of the convolutional tracking pipeline. The ECM employs an attention-like module design, which conducts spatial and channel linear fusion of fused features and enhances the nonlinearly of the fused features. Additionally, it references successful factors of current lightweight trackers and introduces skip-connections and reuse of search area features. The ERH reparameterizes the feature dimensional stage in the standard center head and introduces channel attention to optimize the bottleneck of key feature flows. Comprehensive experiments show that LightFC achieves the optimal balance between performance, parameters, Flops and FPS. The precision score of LightFC outperforms MixFormerV2-S by 3.7 \% and 6.5 \% on LaSOT and TNL2K, respectively, while using 5x fewer parameters and 4.6x fewer Flops. Besides, LightFC runs 2x faster than MixFormerV2-S on CPUs. Our code and raw results can be found at https://github.com/LiYunfengLYF/LightFC

翻译：尽管单目标跟踪器已取得先进性能，但其大规模模型难以应用于资源受限平台。此外，现有轻量级跟踪器仅在参数量、性能、计算量（Flops）和帧率（FPS）之间实现2-3个指标的平衡。为达成这些指标的最优平衡，本文提出一种名为LightFC的轻量级全卷积孪生跟踪器。LightFC采用新型高效互相关模块（ECM）与新型高效重参数化中心头（ERH），增强卷积跟踪管道的非线性表达能力。ECM采用类注意力模块设计，对融合特征进行空间与通道线性融合，并提升其非线性特性；同时借鉴当前轻量级跟踪器的成功要素，引入跳跃连接与搜索区域特征重用。ERH对标准中心头中的特征维度阶段进行重参数化，并引入通道注意力以优化关键特征流的瓶颈。综合实验表明，LightFC在性能、参数量、计算量和帧率之间实现了最优平衡。在LaSOT和TNL2K数据集上，LightFC的精度评分分别比MixFormerV2-S高出3.7%和6.5%，同时参数量减少5倍，计算量降低4.6倍。此外，LightFC在CPU上的运行速度是MixFormerV2-S的2倍。我们的代码与原始结果可在https://github.com/LiYunfengLYF/LightFC获取。