为了解决特征融合目标跟踪(Staple)算法在复杂场景中固定权重融合方式的局限性,在其基础上进行改进并引入通道置信度,提出了一种自适应特征融合与通道加权的抗遮挡相关滤波跟踪算法。先引入多维特征描述,根据每个通道上滤波模板的响应峰值,计算通道权重;再根据特征模型的响应结果,计算模型的可靠性,确定模型的融合权重,从响应结果的角度完成特征融合;最后根据历史帧的平均峰值相关能量,以及当前帧图像与前一帧图像的均方误差,来判断目标的遮挡情况,并进行模型更新。在OTB-2013和OTB-100数据集上进行实验,与Staple算法相比,所提算法的成功率和精确度均有所提高,并在多项具有挑战的属性上表现较佳。

To solve the limitation of the feature fusion target tracking (Staple) algorithm when using the fixed weight fusion method in complex scenes, this study proposes an adaptive-feature fusion and channel weighted anti-occlusion related filtering algorithms with an improved channel confidence. First, to introduce a multi-dimensional feature description, we calculate the channel weights according to the response peak of the filter template on each channel. Then, calculate the reliability of the model based on the response results of the feature model, determine the fusion weight of the model, and complete the feature fusion from the perspective of the response results. Finally, based on the average peak correlation energy of the historical frame and the mean square error of the current and previous frame images, we determine the occlusion of the target and update the model. Comparative experiments with the Staple algorithm are conducted on the OTB-2013 and OTB-100 datasets and the proposed algorithm suggests an improved success rate and accuracy and performs better with respect to many challenging attributes.