Lightweight Full-Convolutional Siamese Tracker
This work addresses the need for efficient object tracking on limited-resource platforms, offering a lightweight solution with competitive performance, though it is incremental as it builds on existing Siamese tracker frameworks.
The paper tackles the problem of single object trackers being too large for resource-limited platforms by proposing LightFC, a lightweight full-convolutional Siamese tracker that achieves an optimal balance between performance, parameters, Flops, and FPS, with precision scores outperforming MixFormerV2-S by 3.7% on LaSOT and 6.5% on TNL2K while using 5x fewer parameters and 4.6x fewer Flops.
Although single object trackers have achieved advanced performance, their large-scale models hinder their application on limited resources platforms. Moreover, existing lightweight trackers only achieve a balance between 2-3 points in terms of parameters, performance, Flops and FPS. To achieve the optimal balance among these points, this paper proposes 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 improve the feature representation of the convolutional tracking pipeline. The ECM uses an attention-like module design, which conducts spatial and channel linear fusion of fused features and enhances the nonlinearity of the fused features. Additionally, it refers to 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 on LaSOT and TNL2K by 3.7 % and 6.5 %, respectively, while using 5x fewer parameters and 4.6x fewer Flops. Besides, LightFC runs 2x faster than MixFormerV2-S on CPUs. In addition, a higher-performance version named LightFC-vit is proposed by replacing a more powerful backbone network. The code and raw results can be found at https://github.com/LiYunfengLYF/LightFC.