Moving Object Detection by Low-Rank Analysis of Region-Based Correlated Motion Fields
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023, 2023-July, pp. 5874-5877
- Issue Date:
- 2023-10-20
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Moving Object Detection by Low Rank Analysis of_Region Based Correlated Motion Fields-Husam AL-NAJJAR.pdf | Published version | 1.14 MB |
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This paper proposes a novel approach for moving object detection in video sequences captured by nonstationary cameras The approach called RCMFD uses region based correlated motion fields decomposition which exploits the sparsity of foreground motions against the low rank structured background motion The method uses spatial correlations of region based features to boost accurate change detection for motion estimation and motion features across object boundaries are used to exploit moving objects A dense optical field which is robust to illumination changes and noise is established using cross correlation of region based features and a robust principal component analysis RPCA model is applied to partition exploited motions into background and foreground motions Experiments demonstrate the robustness of the proposed method on real video sequences
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