基于空间和通道注意力机制的目标跟踪方法

目标跟踪是计算机视觉中重要的研究领域之一，大多跟踪算法不能有效学习适合于跟踪场景的特征限制了跟踪算法性能的提升。该文提出了一种基于空间和通道注意力机制的目标跟踪算法(CNNSCAM)。该方法包括离线训练的表观模型和自适应更新的分类器层。在离线训练时，引入空间和通道注意力机制模块对原始特征进行重新标定，分别获得空间和通道权重，通过将权重归一化后加权到对应的原始特征上，以此挑选关键特征。在线跟踪时，首先训练全连接层和分类器层的网络参数，以及边界框回归。其次根据设定的阈值采集样本，每次迭代都选择分类器得分最高的负样本来微调网络层参数。在OTB2015数据集上的实验结果表明:相比其他主流的跟踪算法，该文所提算法获得了更好的跟踪精度，重叠成功率和误差成功率分别为67.6%，91.2%。

Spatial and Channel Attention Mechanism Method for Object Tracking

Object tracking is one of the important research fields in computer vision. However, most tracking algorithm can not effectively learn the features suitable for tracking scene, which limits the performance improvement of tracking algorithm. To overcome this problem, this paper proposes a target tracking algorithm based on CNN Spatial and Channel Attention Mechanisms (CNNSCAM). The method consists of an off-line training apparent model and an adaptive updating classifier layer. In the offline training, the spatial and channel attention mechanism module is introduced to recalibrate the original features, and the space and channel weights are obtained respectively. The key features are selected by normalizing the weights to the corresponding original features. In online tracking, the network parameters of the full connection layer and classifier layer are trained, and the boundary box regression is used. Secondly, samples are collected according to the set threshold, and the negative sample with the highest classifier score is selected for each iteration to fine tune the network layer parameters. The experimental results on OTB2015 dataset show that compared with other mainstream tracking algorithms, the proposed method achieves better tracking accuracy. The overlap success rate and error success rate are 67.6% and 91.2% respectively.