分块快速压缩追踪算法

Block Fast Compressive Tracking Algorithm

FCT作为一种新的追踪算法,具有简单、高效、实时的优点,但是该算法依旧存在缺点.在FCT中,由于压缩测量矩阵的稀疏性,忽略了样本的空间信息,使得提取的特征不能准确的表征样本;当追踪错误时没有补救措施.本文提出一种改进的快速压缩追踪算法,该方法充分利用样本图像的空间信息,分块提取样本的Haarlike特征;利用目标运动估计法矫正分类错误时追踪到的目标.通过调整压缩测量矩阵中行向量的稀疏度以及朴素贝叶斯分类器的阈值可以实现目标的准确追踪.实验结果表明,与快速压缩追踪算法(FCT)相比,本文改进后的算法,无论是在追踪相似度、追踪成功率还是主观视觉效果上都有所提高.

FCT is a simple yet effective and efficient tracking algorithm, despite much success has been demonstrated, numerous issues remain to be addressed. In FCT, because of the sparsity of the compression measurement matrix, the spatial information of the sample is neglected, so the feature cannot represent the tracking target correctly and there is no remedy when tracking error. In this paper, we propose an improved fast compressive tracking algorithm considering the sample space information and extracts generalized Haar-like features randomly in block; target motion estimation method is used to correct target location, as the classifier is wrong. Adjusting the sparse degree of vector in compression measurement matrix and threshold of naive Bayes classifier can realize accurate target tracking. The experimental results show that compared with FCT, the improved algorithm achieves much better results in terms of both similarity and success rate and subjective visual perception.