基于强化特征学习和表达策略的孪生网络跟踪算法

针对基于全卷积孪生网络跟踪算法在面对相似物干扰、光照变化等复杂环境时容易出现跟踪漂移的问题,本文在分析与实验基础上提出如下特征强化策略。首先,将改良的深度卷积神经网络VGG16引入跟踪框架来提高模型的特征学习能力;其次,针对单一特征无法充分描述目标信息,且对干扰物比较敏感的问题,本文设计一种特征增强模块,由浅至深融合不同层次语义信息来提高特征的表达能力;最后,提出一种轻量级的三元注意力机制,帮助模型自适应关注优势特征,进一步提高了模型在复杂环境下的鲁棒性。将上述策略应用到全卷积孪生网络算法上取得了显著的效果。在OTB100数据集上,本文算法成功率曲线下面积较基准算法提升了15.1%,距离精度提升了16.3%,在复杂环境下也能对目标进行有效跟踪。

Siamese network tracking algorithm based on reinforcement feature learning and expression strategy

Aiming at the problem that the tracking algorithm based on the fully convolutional siamese network is easy to tracking drift in the face of complex environments such as analog interference and illumination changes, this paper proposes the following strategies to optimize features on the basis of analysis and experiments. First, the deep convolutional neural network VGG16 is introduced into the tracking framework to improve the feature extraction ability of the model. Then, aiming at the problem that a single feature cannot adequately describe the target information and is sensitive to interferences, this paper designs a feature enhancement module, which integrates different levels of semantic information from shallow to deep to improve the expressiveness of features. Finally, a lightweight triple attention is proposed to help the model adaptively focus on dominant features and further improve the robustness of the model in complex environments. Applying the above strategies to the fully convolutional siamese network algorithm has achieved remarkable results. On the OTB100 dataset, compared with benchmark algorithm, the area under the success rate curve of the algorithm in this paper is increased by 15.1%, and the distance accuracy is increased by 16.3%, and the target can also be effectively tracked in complex environment.