针对实时单目标追踪问题，利用双边加权最小二乘模糊支持向量机，提出了基于 多特征融合的实时追踪算法． 在所提出的算法中，首先利用局部 HOG 特征和全局颜色特 征分别训练双边加权最小二乘模糊支持向量机，然后利用两个分类器的线性组合实现目 标追踪． 针对基于局部 HOG 特征的分类器，利用基于多个基样本的相关滤波算法克服矩 阵求逆． 针对基于全局颜色特征的分类器，利用独热编码对特征进行编码实现快速计算． 在公开数据集上的实验结果表明:与已有的高性能单目标追踪算法相比，所提出的算法在 形变、快速运动、运动模糊等多个方面均表现出了更优的追踪性能．

A multi-feature fusion-based algorithm was proposed for real-time single object tracking by using the bi- lateral weighted least squares fuzzy support vector machine． In the proposed algorithm，the bilateral weighted least squares fuzzy support vector machine was trained with local HOG feature and global color feature respectively，and object tracking was achieved by using the linear combination of the two classifiers． For the local HOG feature-based classifier，the multiple base samples based correlation filtering was adopted to overcome matrix inversion． For the global color feature based-classifier，the unique thermal coding were used to encode the feature to achieve fast cal- culation． The experimental results on the public data sets show that compared with the state-of-the-art trackers， the proposed algorithm shows better tracking performance in deformation，fast motion，motion blur，and in-plane/ out-of-plane rotation．