相关滤波的运动目标抗遮挡再跟踪技术

针对相关滤波在抗遮挡方面效果不佳的问题，本文在ECO_HC(efficient convolution operators handcraft)的基础上提出了一种多特征融合的抗遮挡相关滤波算法。在相关滤波算法的框架下，将目标ULBP(uniform local binary pattern)纹理特征和目标HOG(histogram of oriented gridients)特征进行线性加权融合；在模型建立与更新阶段通过高斯掩码函数缓解循环移位造成的边界效应；通过计算目标最大响应值的峰值均值比来判断目标状态，并将卡尔曼算法作为目标被遮挡后重定位策略。实验结果显示，在16个视频序列上，该文算法的平均精确度达到87.3%，成功率达到76.5%，相比基线算法，分别提升了27.7%和23.7%。

Anti-occlusion retracking technology for a moving target based on correlation filtering

To address the poor anti-occlusion effect of correlation filtering, this paper proposes an anti-occlusion correlation filtering algorithm by means of multifeature fusion based on efficient convolution operators handcraft. First, based on the framework of correlation filtering, a method of linearly weighted fusion is adopted to deal with the target uniform local binary pattern texture feature and the target histogram of oriented gradients feature. Second, the Gaussian mask function is used during the model establishment and update phase to ease the boundary effect caused by cyclic shift. Lastly, the target state is judged by calculating the peak-to-average ratio of the target maximum response value, and the Kalman algorithm is utilized as the relocation strategy after the target is blocked. Experimental results show that the average accuracy of the proposed algorithm reaches 87.3%, and the success rate reaches 76.5% on 16 test sequences, which are 27.7% and 23.7% higher than those of the baseline algorithm, respectively.