基于在线判别分布域特征选择的鲁棒跟踪算法

摘要

针对基于检测目标跟踪中的特征描述子Haar-like表征能力不强和易引入错误训练样本导致目标漂移的问题,提出了一种利用分布域描述算子进行示例层级的在线判别特征选择跟踪算法.首先,用软直方图方法快速近似得到分布域特征,并利用此描述算子取代Haar-like特征有效表示目标的外观信息.然后,基于示例级样本的先验信息进行有监督学习,利用在线判别特征选择算法选择最佳的分布域层特征以减少漂移现象发生.实验利用多场景视频标准测试库及新的评价指标进行验证,结果表明本文算法性能优于对比算法.%The Haar-like features used in MIL ( multiple instance learning ) trackers are not efficient to represent the appearances of the targets, and the noise samples are prone to be involved for classifier training phase, then drift in targets may happen. To solve these problems, an online discriminative feature selection ( ODFS ) tracking algorithm based on distribution fields ( DFs ) descriptors at instance level was proposed. Firstly, soft histogram method is manipulated to fastly approximate DFs, and the Haar-like features are replaced with the layers of DFs, which are adopted to represent appearance information. Then, supervised learning with prior information of instance labels is conducted;the ODFS algorithm is used to select the most optimal discrimination layer features, which can handle drift more effectively. The proposed tracking method are tested in benchmark dataset of a large variety of scenarios and under new evaluation indexes. Experimental results show the effectiveness of the algorithm.