整体特征通道识别的自适应孪生网络跟踪算法

针对孪生网络目标跟踪算法仅使用特征提取网络提取特征，在遮挡、旋转、光照与尺度变化中容易出现跟踪失败的问题，提出整体特征通道识别的自适应孪生网络跟踪算法. 将高效的通道注意力模块引入ResNet22孪生网络中，提高特征的判别能力. 使用整体特征识别功能计算全局信息，提取更为丰富的语义信息，提高跟踪算法精度. 同时，引入自适应模板更新机制，解决遮挡与长期跟踪导致的模板退化问题. 为了验证所提方法的有效性，在OTB2015、VOT2016与VOT2018等公开数据集上进行测试，并与其他跟踪算法进行对比. 结果表明，所提算法在精确度与成功率上表现较好，在背景杂乱、旋转、光照与尺度变化等情况中表现稳定.

An adaptive siamese network tracking algorithm based on global feature channel recognition

Siamese network target tracking algorithm only uses the feature extraction network to extract features, leading to tracking failures in occlusion, rotation, illumination and scale changes. An adaptive siamese network tracking algorithm with global feature channel recognition was proposed. The efficient channel attention module is introduced into the ResNet22 siamese network to improve the ability to distinguish features. The global feature recognition function is used to calculate global information, extract richer semantic information, and improve the accuracy of tracking algorithms. At the same time, an adaptive template update mechanism is introduced to solve the problem of template degradation caused by occlusion and long-term tracking. In order to verify the effectiveness of the proposed method, the proposed method was tested on public data sets such as OTB2015、VOT2016 and VOT2018, and compared with other tracking algorithms. Results show that the proposed algorithm performs well in accuracy and success rate. The proposed method is stable under background clutter, rotation, as well as illumination and scale changes.