提出一种多任务稳健主成分分析方法，用以结合多视觉特征实现运动目标分割. 给定由多类型特征矩阵描述的视频数据，将它分解为低秩和稀疏部分，其中的稀疏部分对应于运动目标. 该矩阵分解过程是一个凸优化问题，通过用ALM方法最小化核范数和`2,1-范数的约束组合. 与仅利用单类型特征的方法相比，本文提出的方法能够结合多类型特征，因此可获得更加精确可靠的结果. 对HumanEva和Change Detection两个数据集的实验表明了该方法的有效性.
This paper proposes a new algorithm, multi-task robust principal component analysis (MTRPCA),to collaboratively integrate multiple visual features for motion segmentation. Given the video data described by multiple features, the motion parts are obtained by jointly decomposing multiple feature matrices into pairs of low-rank and sparse matrices. The inference process is formulated as a convex optimization problem that minimizes a constrained combination of nuclear norm and `2,1-norm. The convex optimization problem can be solved efficiently with an augmented Lagrange multiplier (ALM) method. Compared with previous methods based on individual features, the proposed method seamlessly integrates multiple features within a single inference
step, and thus produces more accurate and reliable results. Experiments on human motion data sets, Human Eva and change detection, show that the proposed MTRPCA is effective and promising.
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