Robust Principal Component Analysis via Symmetric Alternating Direction for Moving Object Detection
| Autor: | Zhenzhou Shao, Gaoyu Wu, Yong Guan, Jindong Tan, Zhiping Shi, Ying Qu |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
Structure (category theory) 0102 computer and information sciences 02 engineering and technology 01 natural sciences Object detection Separable space Constraint (information theory) 010201 computation theory & mathematics Singular value decomposition Convergence (routing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Interrupt Algorithm Robust principal component analysis |
| Zdroj: | Advances in Multimedia Information Processing – PCM 2017 ISBN: 9783319773827 PCM (2) |
| DOI: | 10.1007/978-3-319-77383-4_27 |
| Popis: | Robust Principal Component Analysis (RPCA) has been proved to be effective for the moving object detection with background variation. Alternating Direction Method (ADM) based RPCA takes full advantages of the separable structure of the objective function to achieve better results than traditional RPCA methods. But it suffers from the heavy computing burden and low efficiency. In this paper, a Symmetric Alternating Direction Method (SADM) is proposed to solve above problems. SADM optimizes the iterative strategy of ADM by updating the multiplier of the linear constraint twice every iteration which speeds up the convergence, thus reduces the execution times of Singular Value Decomposition (SVD). Besides, the new equilibrium parameter and interrupt mechanism are introduced to guarantee the object detection accuracy and avoid the unnecessary iterations. Compared with ADM, the experimental results show that not only the detection accuracy of proposed method is improved by 46.8%, but also the time consumption is reduced by 97.5%. |
| Databáze: | OpenAIRE |
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