多模型融合的孪生网络视觉跟踪

Multimodel Integrated Siamese Network Visual Tracking

为提升孪生网络视觉跟踪算法的准确性,提出一种融合多任务差异化同质型模型的孪生网络视觉跟踪算法。首先在决策层对孪生网络视觉跟踪模型与目标分割模型进行融合,然后结合多尺度搜索区域、目标上下文特征、多学习率模型更新策略进行跟踪。在标准数据集VOT、OTB、LaSOT、UAV123上进行算法评估。实验结果表明,所提算法在遮挡、快速运动、光照变化等干扰下可以稳定跟踪目标。

A siamese network visual tracking algorithm based on fusion multitask differentiated homogeneous models is proposed to improve the accuracy of the algorithm.First,the siamese network visual tracking and target segmentation models are fused in the decision-making layer.Then,they are combined with multiscale search area,contextual features,and multilearning rate model updating strategy to track.Different algorithms are evaluated using standard datasets,namely,VOT,OTB,LaSOT,and UAV123.Experimental results show that the proposed algorithm can stably track the object under the interference of occlusion,fast motion,and illumination change,among others.