鲁棒性在线子空间辨识与跟踪的关键问题研究

项目名称： 鲁棒性在线子空间辨识与跟踪的关键问题研究

项目编号： No.61203273

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 何军

作者单位： 南京信息工程大学

项目金额： 25万元

中文摘要： 在数据高度缺失、甚至数据受异常噪声污染的苛刻条件下，快速从高维数据中辨识出低秩子空间并进行子空间跟踪，是本项目的主要研究内容。本项目将研究格拉斯曼流形的随机梯度下降最优化理论，研究在数据缺失情况下1-范数最优化模型的增广拉格朗日形式，通过选择合适的子空间辨识问题代价损失函数，由此进行随机梯度下降算法的推导及收敛性证明。本项目将从视频监控中实时背景/前景分离，人脸序列图像的在线对准两方面，研究鲁棒性子空间在线辨识与跟踪在计算机视觉问题中的应用，并开发出示范系统验证算法的有效性与实时性。

中文关键词： 子空间跟踪；在线学习；鲁棒性优化算法；计算机视觉；视觉跟踪

英文摘要： The major research of this proposal is to introduce a fast online algorithm which can robustly identify and track the low-rank subspace from highly incomplete high-dimensional data which are also corrupted by sparse outliers. From the algorithmic aspect, this proposal intends to study the optimization framework of Grassmannian manifold, and investigate the L-1 norm least absolute regression model from partial observed data, which is aiming for leveraging the corruption by sparse outliers. This proposal mainly focuses on regarding the augmented Lagrangian of the least absolute regression model as the subspace loss function and intends to incorporate this loss function into the Grassmannian stochastic gradient descent framework. From the theoretical aspect, this proposal will give a rigorous analysis that given the corruption fraction of uniformly distributed sparse outliers, how much the low bound of the missing data ratio is. And also the proof of the global optimum convergence of this proposed online robust subspace identification algorithm will be given. From the application aspect, this proposal intends to apply the proposed theory and algorithm to the task of real-time separating moving objects from background in video surveillance. And This proposal will also dig into the novel approach for online robust f

英文关键词： subspace tracking；online learning；robust optimization algorithms；computer vision；visual tracking