目的 提出结合目标预测位置的压缩跟踪算法用于提高目标跟踪的准确度。方法 选择随机间距稀疏Toeplitz矩阵作为投影矩阵，对原始多尺度Haar-like特征进行压缩；然后，将样本与Mean Shift算法框架下的预测位置的距离权重输入Bayes分类器，形成分类背景与目标的判别函数；最后对参数的更新方式进行优化，提出了参数自适应的学习模式。结果 与目前较流行的6种目标跟踪算法在20个具有挑战性的序列中进行比较，实验结果表明本文提出的算法平均跟踪成功率比压缩跟踪算法将近高27%，平均运行时间为0.15 s/帧。结论 本文采用结合预测位置的压缩跟踪算法，在参数更新阶段采用了非线性参数学习模式，实验结果表明结合目标预测位置的跟踪算法比一般的跟踪算法更具有鲁棒性，更能适应遮挡等情况，跟踪的效果也更加平滑。

Objective An effective object tracking method by combining the predicted target position with compressive tracking (CPCT) is proposed. Method Sparse Toeplitz projection matrices with random pitches are used to extract the compression feature of the original multi-scale Haar-like feature. Then, the distance between the sample positions and the predicted positions of the Mean Shift algorithm is used as object candidates' weights and the weighted Bayes classifier is used to determine the reliable object position. An adaptive parameter updating approach is used in the classifier training. Result The experimental results have shown that the average success rate of the CPCT tracking algorithm is about 27% higher than that of compressive tracking(CT), and the tracking speed is about 0.15 second per frame on average on 20 challenging sequences. Conclusion CPCT tracking algorithm is more robust and smoother compared with six state-of-the-art algorithms on 20 challenging sequences by combining the predicted target position with compressive tracking and adaptive parameter updating approach.