通道稳定性加权补充学习的实时视觉跟踪算法

为解决补充学习（Staple）跟踪算法在平面内旋转、部分遮挡时存在的跟踪失败问题，提出了一种通过通道稳定性加权的补充学习（CSStaple）跟踪算法。首先，使用标准相关滤波分类器检测出每层通道的响应值；然后，计算获得每层通道的稳定性权重，并乘到每层权重上，获得相关滤波响应；最后，通过融合颜色补充学习器的响应，得到最终的响应结果，响应中的最大值的位置即为跟踪结果。将所提算法与层和空间可靠性判别相关滤波（CSR-DCF）跟踪、对冲深度跟踪（HDT）、核化相关滤波（KCF）跟踪和Staple等跟踪算法进行了对比实验。实验结果表明，所提算法在成功率上表现最优，在OTB50和OTB100上比Staple分别高出2.5个百分点和0.9个百分点，验证了所提算法对目标在平面内旋转和部分遮挡时的有效性。

Real-time visual tracking algorithm via channel stability weighted complementary learning

In order to solve the problem of tracking failure of the Sum of template and pixel-wise learners (Staple) tracking algorithm for in-plane rotation and partial occlusion, a simple and effective Channel Stability-weighted Staple (CSStaple) tracking algorithm was proposed.Firstly, a standard correlation filter classifier was employed to detect the response value of each channel. Then, the stability weight of each channel was calculated and multiplied to the weight of each layer to obtain correlation filtering response. Finally, by integrating the response of the color complementary learner, the final response result was obtained, and the location of the maximum value in the response was the tracking result. The proposed algorithm was compared with several state-of-the-art tracking algorithms including Channel and Spatial Reliability Discriminative Correlation Filter (CSR-DCF) tracking, Hedged Deep Tracking (HDT), Kernelized Correlation Filter (KCF) Tracking and Staple. The experimental results show that, the proposed algorithm performs best in the success rate, it is 2.5 percentage points higher and 0.9 percentage points higher than Staple on OTB50 and OTB100 respectively, which proves the effectiveness of the proposed algorithm for target in-plane rotation and partial occlusion.