结合特征筛选与二次定位的快速压缩跟踪算法

针对压缩跟踪算法易受遮挡影响和模型更新比较盲目的问题，提出结合特征筛选与二次定位的快速压缩跟踪算法（Fast compressive tracking algorithm combining feature selection with secondary localization，FSSL-CT）.首先，对全局区域划分子区域，从中提取压缩特征，根据正、负样本估计出各个压缩特征在正、负类中的分布; 然后，使用自适应学习率结合正类更新阈值对分类器模型进行更新; 最后，将跟踪分为两个阶段，每个阶段在对应的搜索区域内采集候选样本，并从全部特征中筛选出部分优质特征加权构建分类器，通过分类候选样本最终完成目标跟踪.在8个公共测试序列和4个自制序列中与最近提出的两个代表性算法进行比较，本文算法在大多数测试序列中都具有最高的跟踪成功率和最低的平均中心误差，平均处理速度可以达到3.04毫秒/帧.实验结果表明，本文算法具有更好的抵抗短时遮挡的能力，更高的准确性和鲁棒性，以及良好的实时性.

Fast Compressive Tracking Algorithm Combining Feature Selection with Secondary Localization

As the traditional compressive tracking algorithm fails to track targets stably under occlusive condition and update model accurately, a fast tracking algorithm combining feature selection with secondary localization based on compressive tracking (FSSL-CT) is proposed. Firstly, compressive features are extracted from sub-regions partitioned from the global region, and the distributions of each compressive feature in positive and negative classes are estimated. Secondly, the classifier model is updated utilizing the method of adaptive learning rate and positive class update threshold. Finally, the tracking stage is divided into two procedures. In each procedure, some candidate samples are collected in the given searching region, and partial high quality features are selected from all the features and weighted to construct a classifier, then, the candidate samples are classified by the classifier. After that, the target tracking is achieved. Compared with two state-of-the-art algorithms on 8 public testing sequences and 4 private sequences, the FSSL-CT algorithm is proved to have the highest tracking success ratio and the lowest average central error in most of the sequences, and the average processing speed could achieve 3.04 milliseconds per frame. It is tested that the proposed FSSL-CT algorithm has a better capacity of resisting short-time occlusion and running in real-time, higher accuracy and robustness than the two state-of-the-art algorithms.