提出了一种新的简单高效记忆梯度协方差跟踪算法。首先, 利用记忆梯度算法优化协方差目标模板与候选目标的距离函数以快速精确搜索最佳匹配目标, 充分利用记忆梯度算法避免收敛到局部最优解, 克服最速下降算法中穷尽局部搜索的低效。同时, 为减少黎曼流形空间上高维正定对称协方差矩阵相似性度量的计算负担, 用JBLD(Jensen-Bregman LogDet)方法进行协方差特征的相似性度量。该度量在基于梯度优化算法的框架下有助于梯度的快速计算。实验利用多场景视频标准测试库及新的评价指标, 验证了文中算法性能优于对比算法。

A novel, simple and efficient memory gradient covariance tracking algorithm is proposed, which can optimize the distance function between the covariance target mould and the candidate target to search the best matched target quickly and accurately. In order to overcome the low efficiency of the exhaustive local searching in steepest descent algorithm, the memory gradient algorithm is taken full advantages to avoid converging to local optimal point. To reduce the calculation burden of the similarity metric for high dimensional positive symmetric covariance matrices under Riemannian space, Jensen-Bregman LogDet (JBLD) divergence metric is utilized to measure the similarity of covariance features. Besides that, the JBLD metric contributes to fast computation of the gradient under the framework of the gradient optimization algorithm. In the experiment, multi-scenario video standard testing library and new evaluation indexes are used. The experiment results show that the performance of the algorithm is better than compared algorithms.