关键词: 运动目标检测, 低秩稀疏分解, 自适应的鲁棒主成分分析

Abstract:  Focusing in the precise detection of moving objects in video processing, this paper proposes an adaptive low-rank and sparse decomposition algorithm. It constructs an augmented matrix by background model and to-be-computed background vector, and executes low-rank and sparse decomposition to dimension-decreasing augmented matrix by RPCA (robust principal component analysis) and obtains low-rank part and sparse noise which corresponding to background and moving foreground. Then it updates background model by new background vectors using incremental SVD (singular value decomposition). The experimental results indicate that the proposed method can process complicated scenes including illumination change and background moving better, and reduces the latency of algorithm and memory occupation at meanwhile.

Key words: detection of moving objects, low-rank and sparse decomposition, adaptive robust principal component analysis