Ding Yuxuan, Miao Kehua
Department of Automation, Xiamen University, Xiamen 361102, China.
Sensors (Basel). 2024 Dec 21;24(24):8171. doi: 10.3390/s24248171.
Recent advancements in the field of object tracking have been notably influenced by Siamese-based trackers, which have demonstrated considerable progress in their performance and application. Researchers frequently emphasize the precision of trackers, yet they tend to neglect the associated complexity. This oversight can restrict real-time performance, rendering these trackers inadequate for specific applications. This study presents a novel lightweight Siamese network tracker, termed SiamGCN, which incorporates global feature fusion alongside a lightweight network architecture to improve tracking performance on devices with limited resources. MobileNet-V3 was chosen as the backbone network for feature extraction, with modifications made to the stride of its final layer to enhance extraction efficiency. A global correlation module, which was founded on the Transformer architecture, was developed utilizing a multi-head cross-attention mechanism. This design enhances the integration of template and search region features, thereby facilitating more precise and resilient tracking capabilities. The model underwent evaluation across four prominent tracking benchmarks: VOT2018, VOT2019, LaSOT, and TrackingNet. The results indicate that SiamGCN achieves high tracking performance while simultaneously decreasing the number of parameters and computational costs. This results in significant benefits regarding processing speed and resource utilization.
基于暹罗网络的跟踪器对目标跟踪领域的最新进展产生了显著影响，这些跟踪器在性能和应用方面都取得了长足的进步。研究人员经常强调跟踪器的精度，但往往忽略了其相关的复杂性。这种疏忽可能会限制实时性能，使这些跟踪器在特定应用中表现不佳。本研究提出了一种新型的轻量级暹罗网络跟踪器，称为SiamGCN，它结合了全局特征融合和轻量级网络架构，以提高在资源有限的设备上的跟踪性能。选择MobileNet-V3作为特征提取的主干网络，并对其最后一层的步长进行了修改，以提高提取效率。基于Transformer架构开发了一个全局相关模块，利用多头交叉注意力机制。这种设计增强了模板和搜索区域特征的整合，从而促进了更精确和更具弹性的跟踪能力。该模型在四个著名的跟踪基准上进行了评估：VOT2018、VOT2019、LaSOT和TrackingNet。结果表明，SiamGCN在实现高跟踪性能的同时，减少了参数数量和计算成本。这在处理速度和资源利用方面带来了显著优势。
