融合残差连接与通道注意力机制的Siamese目标跟踪算法

针对Siamese跟踪算法在目标形变、相似物体干扰等复杂情况下容易跟踪漂移或丢失的问题,提出一种融合残差连接与通道注意力机制的目标跟踪算法.首先,通过残差连接将模板分支网络提取的浅层结构特征与深层语义特征进行有效的融合,以提高模型的表征能力;其次,引入通道注意力模块,使模型自适应地对不同语义目标特征通道加权,以提高模型...

Siamese Object Tracking Algorithm Combining Residual Connection and Channel Attention Mechanism

Aiming at the problem that Siamese tracking algorithm is easy to track drift or loss in complex situations such as target deformation and similar object interference,a target tracking algorithm combining residual connection and channel attention mechanism is proposed.First,the shallow structure features and the deep semantic features extracted from the template branch network are effectively fused through residual connections to improve the model’s representational ability.Second,the channel attention module is introduced to make the model adaptively weighted to different semantic target feature channels to improve the generalization ability of the model.Finally,a weight mask based on correlation response values is designed and proposed to increase the weight of similar semantic target loss values during offline training,so that the model is enhanced discrimination of similar semantic targets in end-to-end offline learning.The results from comparative experiments with mainstream tracking algorithms on standard tracking datasets OTB,Temple-Color128,VOT2016 and VOT2018 show that the algorithm is highly competitive in tracking accuracy and success rate,with superior real-time performance and reliability.