Abstract: For the object tracking problems in computer vision, feature Weighted Fusion online Multiple Instance Learning tracking algorithm（WFMIL） is proposed. WFMIL trains two features（Hog and Haar） classifier separately by multiple instance learning method. In the tracking process, they are integrated into a strong classifier by the linear operation. While in the learning process, weight is introduced into instances of positive package. Experimental results show that WFMIL can solve the object drift and has a certain robustness in handling occlusion, target abrupt motion, illumination change, and motion blur.

Key words: feature fusion, online multiple instance learning, object tracking

摘要： 为了能更加准确鲁棒地跟踪目标，提出了特征加权融合的在线多示例学习跟踪算法（WFMIL）。WFMIL在多示例学习框架下分别训练两种特征（Hog和Haar）分类器。在跟踪过程中，通过线性运算融合成一个强分类器，同时在学习过程中对正包中的示例引入权重。实验结果统计表明WFMIL能很好地解决目标漂移问题，并且对目标遮挡、运动突变、光照变化以及运动模糊等具有较好的鲁棒性。

关键词: 特征融合, 在线多示例学习, 目标跟踪