基于注意力机制的Siamese目标跟踪算法研究

Research on Siamese Target Tracking Algorithm Based on Attention Mechanism

为了进一步提升Siamese神经网络算法在目标跟踪领域的性能,本文对SiamMask的backbone模型基于注意力机制原理进行了重新设计。首先,对Siamese目标追踪网络的backbone网络框架进行局部修改;其次,对改进的算法与原SiamMask算法在Microsoft COCO2017数据集上进行了网络训练与验证;最后,将原SiamMask算法与改进的算法的验证集数据进行比对,以此来评价改进算法的性能。结果表明,在同等算力与数据集的条件下,基于注意力机制的backbone Siamese目标跟踪算法比SiamMask在IOU值上有2个百分点左右的性能提升。

In order to further improve the performance of Siamese for the object tracking,in this paper,the backbone of SiamMask was redesigned on the attention mechanism.Firstly,the frame of the backbone of Siamese was modified.Secondly,the improved algorithm and the original SiamMask algorithm were trained and verified in the Microsoft COCO 2017 dataset.Finally,in the Validation set,the original SiamMask algorithm was compared with the improved algorithm to evaluate the performance of the improved algorithm.The results show that under the condition of the same ability of computing and datasets,the Siamese based on attention mechanism has about 2%improvement of IOU over SiamMask.