基于记忆梯度追踪的高效稀疏跟踪算法  

Efficient Sparse Tracking Based on Memory Gradient Pursuit

作　　者：郭强[1,2] 孙鹏[3,4] 赵迎春[2] 吴成东[1] 

机构地区：[1]东北大学信息科学与工程学院,沈阳110819 [2]中国刑事警察学院图书馆,沈阳110854 [3]中国刑事警察学院网络安全执法和公安信息化协同创新中心,沈阳110854 [4]中国刑事警察学院声像资料检验技术系,沈阳110854

出　　处：《计算机辅助设计与图形学学报》2016年第4期565-572,共8页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61307016;61273078);公安部应用创新计划项目(2012YYCXXJXY129);辽宁省教育厅一般项目(L2015558)

摘　　要：为了实现快速稳定的L1稀疏跟踪,提出一种基于记忆梯度追踪的优化稀疏表示目标跟踪算法.首先采用整合后的分块协方差特征对目标外观建模,构建出更有效的适于稀疏跟踪框架的观测模型,结合模板更新策略可提高对复杂场景干扰和漂移模板的鲁棒性;然后采用更低计算成本的自适应比例无迹变化方法近似协方差特征,将流形空间特征相似度量转为欧氏空间度量;最后利用快速记忆梯度追踪方法重构信号性快速稳定的优点减少L1目标跟踪算法稀疏系数的重建时间,计算目标的稀疏解.在各种场景下与5种算法比较的实验结果表明,该算法具有更好的性能.TheL1 trackers are robust to moderate occlusion but computationally expensive, and the oversim-plified descriptors are prone to be drift by the noise. To solve these problems, an optimized sparse represen-tation tracking algorithm based on memory gradient pursuit is proposed. Firstly, effective block covariance descriptors are manipulated to represent appearance model. On the basis of the descriptors with templates updated strategy, the robustness to complex scenes and model drift is improved. Moreover, an adaptive scaled unscented transform method with lower computation cost is adopted to approximate the covariance matrix. Then, similarity metric of covariance descriptor is transferred from manifold to Euclidean space. The algorithm takes advantages of fast and stable convergence of memory gradient algorithm to reduce the re-construction time of sparse coefficient. After that, sparse coefficients are achieved. The experimental results show that the proposed algorithm outperforms other five algorithms.

关 键 词：稀疏表示 方向追踪 记忆梯度 目标跟踪 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]