高质量模板在线更新的孪生网络目标跟踪算法

Siamese network object tracking algorithm with high quality template online updating

为降低孪生网络跟踪器中预定义锚框对模板更新和跟踪精度的影响,提出一种采用高质量模板在线更新的孪生网络目标跟踪算法。在孪生网络跟踪框架的基础上引入一个基于区域建议网络的重检测模块,获得质量更高的回归边界框,利用初始帧、累计帧和当前帧的特征模板训练一个更新网络实现更高质量的模板更新。在OTB100和GOT-10k数据集上的对比实验结果表明,提出算法具有更好的成功率和精度,在具有各种复杂属性的场景中表现出更优秀的性能。

To reduce the influence of predefined anchors in siamese network tracker on template updating and tracking precision,a siamese network object tracking algorithm with high quality template online updating was proposed.Based on the Siamese network tracking framework,a redetection module based on region proposal network was proposed to obtain regression boundary box with higher quality,and an update network was trained using the feature template of the initial frame,cumulative frame and the current frame to achieve higher quality template updating.The comparative experimental results on OTB100 and GOT-10K datasets show that the proposed algorithm has better accuracy and success rate,and also performs better in scenarios with various attributes.