Background recovery from video sequences via online motion-assisted RPCA
Background recovery from video sequences via online motion-assisted RPCA
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DOI：
10.1109/vcip.2016.7805552
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Jiaoru Yang;Jingyu Yang;Xuemeng Yang;Huanjing Yue
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文献类型：
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作者：
Jiaoru Yang;Jingyu Yang;Xuemeng Yang;Huanjing Yue
背景建模是视频分析中的一项重要技术。与运动信息辅助的鲁棒主成分分析（RPCA）已经显示出改进的背景恢复性能，但由于批处理模式的制定和实施，在处理流视频中仍然存在不足。提出了一种在线运动辅助鲁棒主元分析（OMA-RPCA）模型，用于视频序列背景恢复。低秩近似的固有批模式核范数被替换为显式低秩矩阵分解。通过光流法提取的运动信息被纳入到数据项中，以便于从背景中分离运动对象。提出的模型有效地解决了交替优化方案在线模式。实验结果表明，该方法优于国家的最先进的方法具有较低的内存成本和可扩展性的在线应用程序。
Background modeling is an important technique for video analysis. Robust principal component analysis (RPCA) assisted with motion information has shown improved background recovery performance, but still suffers from the deficiency in handling steaming video due to the batch-mode formulation and implementation. This paper proposes an online motion-assisted robust principal component analysis (OMA-RPCA) model for background recovery from video sequences. The inherent batch-mode nuclear norm for low-rank approximation is replaced with an explicitly low-rank matrix factorization. Motion information extracted by an optical flow method is incorporated into the data term to facilitate the separation of moving objects from the background. The proposed model is effectively solved by an alternating optimization scheme in an online mode. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods with lower memory cost and scalability to online applications.