基于深度特征与局部约束掩膜的相关滤波跟踪算法

为提升相关滤波算法在目标遮挡、快速运动以及背景杂乱等情况下跟踪结果的精确度和鲁棒性，提出了一种基于深度特征与局部约束掩膜(Local constrained mask, LCM)的相关滤波跟踪算法。在鉴别性相关滤波跟踪算法的基础上，利用学习得到的二值矩阵作为LCM对滤波器的能量分布进行裁剪，对模板边缘与测试图像之间产生的响应值进行抑制，实现扩大目标搜索区域的同时降低边界效应对跟踪结果的影响；将深度特征引入到特征提取过程中，通过对目标样本进行旋转、翻折和高斯模糊等处理，扩充训练样本数量，使模板学习到更为丰富的目标信息。与主流算法进行对比实验，验证了本文算法在处理目标遮挡、背景嘈杂以及光照变化等干扰时的鲁棒性。

Deep Features with Local Constrained Mask Based Correlation Filter Tracking Algorithm

To improve the tracking accuracy and robustness of correlation filter based trackers, especially with challenging factors such as occlusion, fast motion, background clutter and so forth, an improved correlation filter tracking algorithm based on deep features with local constrained mask is proposed. Based on discriminative correlation filter tracking algorithms, the learned binary matrix is proposed as a local constrained mask to achieve pruning the filter energy, which suppresses the response map generated by the template edge and the testing images. This allows the proposed method to expand the target search region and effectively alleviates the boundary effects caused by circular shifted training samples. Deep features are introduced in the process of feature extraction. By exploiting rotation, flipping, and Gaussian blur operations, the training sample set is expanded, which makes feature templates learn more target information. Compared the robustness of our algorithm with the mainstream methods under distractions like occlusion, background clutter and illumination changes.