尺度自适应特征压缩跟踪

为在复杂环境中对目标进行长时间精确跟踪,提出一种尺度自适应特征压缩跟踪算法。通过结构约束性采样,获取不同尺度不同位置的扫描窗,离线计算不同尺度下的稀疏随机感知矩阵。在线跟踪时利用这些矩阵感知对应尺度的图像采样块,实现特征降维,提高运算速度。利用朴素贝叶斯分类器对降维特征判决,在线学习更新分类器参数,找出具有最高分类得分的采样块作为新的跟踪结果,实现跟踪位置及尺度的自适应更新。实验结果表明,该算法能适应目标的基本姿态变化及尺度缩放,不依赖于目标初始跟踪区域尺度选取,跟踪结果具有较强的鲁棒性。

The scale adaptive feature compressed tracking

In order to track target accurately during a long term in complicated environment, an adaptive scale feature compressed tracking algorithm is presented in the paper. A number of scanning windows with different scales and positions are obtained by construction constraint sampling. To reduce the feature dimension and improve processing speed, the sparse random perceived matrices of different scales which can be easily computed offline are adopted to extract the features of different sampling image patches with relevant scales online.The sampling patch having a maximal classification score is regarded as the new tracking result by classifying the compressing feature via a naive bayes classifier and updating the parameters through online learning,which realize the adaptive update of tracking location and scales. Experimental results show that the algorithm can adapt the basic attitude and scale change, which is robust and does not depend on the scale selection of the initial tracking area .