论文标题
IG-Track：IOU指导的暹罗网络用于视觉对象跟踪
IG-TRACK: IOU Guided Siamese Networks for visual object tracking
论文作者
论文摘要
最近，基于深度学习的暹罗网络，带有用于视觉对象跟踪的区域建议变得越来越流行。在测试时，这些网络在训练的网络上执行额外的计算，以预测边界框。但是，这阻碍了边界框的精度。在这项工作中，作者提出了一个在培训时以交叉路口（IOU）为指导的网络，以预测精确的边界框。这是通过在培训网络中添加新的损失功能来实现的，以最大程度地利用地面真相的预测边界框。在对vot2018进行测试时，GOT-10K跟踪基准测试，拟议的方法在精度方面超过了10％以上的基础方法。
Recently Deep Learning based Siamese Networks with region proposals for visual object tracking becoming more popular. These networks, while testing, perform extra computations on output if trained network, to predict the bounding box. This however hampering the precision of bounding box. In this work, the authors have proposed a network guided by Intersection Over Union(IOU) while training, to predict precise bounding box. This is achieved by adding new loss function in training the network, to maximize IOU of the predicted bounding box with ground truth. While testing on VOT2018, GOT-10k tracking benchmarks,the proposed approach out-performed the base approach by more than 10% in terms of precision.
