Abstract: An advanced Real-time Compressive Tracking（RCT） is proposed. By analyzing low-dimensional features, an advanced random measurement matrix is designed based on RCT, which gives a balanced reflecting to gray level features and texture features. Low-dimensional feature vectors are transformed by using the advanced random measurement matrix. Then it classifies the image sequence into two categories（foreground and background） by using Naive Bayes Classifier. Thus, the tracking of the target is realized. Making a comparison among CT, BCT（a known improved algorithm based on CT） and RCT, according to tracking simulation results to different image sequences, it turns out that RCT shows very robust tracking results without losing the real-time character.

Key words: Real-time Compressive Tracking（RCT）, random measurement matrix, low-dimensional feature vectors

摘要： 提出了一种改进的实时压缩跟踪算法（RCT）。该算法基于实时压缩跟踪算法，构造出一个改进的随机测量矩阵，使降维后得到的压缩特征包含的灰度特征信息和纹理特征信息比例相等。RCT算法首先将图像序列的特征用改进的随机测量矩阵转化为低维度特征，再用朴素贝叶斯分类器对低维特征进行目标和背景的分类，从而实现对目标的跟踪。将原始算法（CT）、一种改进算法（BCT）和该文创新的改进算法（RCT）进行对比，实验表明：RCT算法保持了原始算法的实时性，并且在各实验图像序列中跟踪目标的鲁棒性最好。

关键词: 实时压缩跟踪, 随机测量矩阵, 低维特征