Background subtraction based on random superpixels under multiple scales for video analytics
Background subtraction is a fundamental problem of computer vision, which is usually the first step of video analytics to extract the interesting region. Most previously available region-based background subtraction methods ignore the similarity between the pixels, meaning that the information gaine...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/87527 http://hdl.handle.net/10220/45446 |
| _version_ | 1811695696822140928 |
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| author | Fang, Weitao Zhang, Tingting Zhao, Chenqiu Soomro, Danyal Badar Taj, Rizwan Hu, Haibo |
| author2 | Institute for Media Innovation |
| author_facet | Institute for Media Innovation Fang, Weitao Zhang, Tingting Zhao, Chenqiu Soomro, Danyal Badar Taj, Rizwan Hu, Haibo |
| author_sort | Fang, Weitao |
| collection | NTU |
| description | Background subtraction is a fundamental problem of computer vision, which is usually the first step of video analytics to extract the interesting region. Most previously available region-based background subtraction methods ignore the similarity between the pixels, meaning that the information gained from the pixels that do not contribute, or even contribute negatively to understanding an image, is taken into account. A new background subtraction model based on random superpixel segmentation under multiple scales is proposed. A custom region segmentation area is replaced with a superpixel segmentation area that uses similarity characteristics for pixels in the superpixel area. The compactness of the pixels in the same superpixel area means that the pixels positively contribute to understanding an image compared with when using custom region pixels. Superpixel segmentation is performed using the random simple linear iterative cluster method. Taking random samples during the superpixel segmentation process produces the Matthew effect, thus improving the robustness and efficiency of the model. Multi-scale superpixel segmentation is therefore guaranteed to give more accurate results. Standard benchmark experiments using the proposed approach produced encouraging results compared with the results given by previously available algorithms. |
| first_indexed | 2024-10-01T07:27:35Z |
| format | Journal Article |
| id | ntu-10356/87527 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:27:35Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/875272020-09-26T21:54:20Z Background subtraction based on random superpixels under multiple scales for video analytics Fang, Weitao Zhang, Tingting Zhao, Chenqiu Soomro, Danyal Badar Taj, Rizwan Hu, Haibo Institute for Media Innovation Computer Vision Motion Detection Background subtraction is a fundamental problem of computer vision, which is usually the first step of video analytics to extract the interesting region. Most previously available region-based background subtraction methods ignore the similarity between the pixels, meaning that the information gained from the pixels that do not contribute, or even contribute negatively to understanding an image, is taken into account. A new background subtraction model based on random superpixel segmentation under multiple scales is proposed. A custom region segmentation area is replaced with a superpixel segmentation area that uses similarity characteristics for pixels in the superpixel area. The compactness of the pixels in the same superpixel area means that the pixels positively contribute to understanding an image compared with when using custom region pixels. Superpixel segmentation is performed using the random simple linear iterative cluster method. Taking random samples during the superpixel segmentation process produces the Matthew effect, thus improving the robustness and efficiency of the model. Multi-scale superpixel segmentation is therefore guaranteed to give more accurate results. Standard benchmark experiments using the proposed approach produced encouraging results compared with the results given by previously available algorithms. Published version 2018-08-03T06:11:58Z 2019-12-06T16:43:46Z 2018-08-03T06:11:58Z 2019-12-06T16:43:46Z 2018 Journal Article Fang, W., Zhang, T., Zhao, C., Soomro, D. B., Taj, R., & Hu, H. (2018). Background subtraction based on random superpixels under multiple scales for video analytics. IEEE Access, 6, 33376-33386. https://hdl.handle.net/10356/87527 http://hdl.handle.net/10220/45446 10.1109/ACCESS.2018.2846678 en IEEE Access © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 11 p. application/pdf |
| spellingShingle | Computer Vision Motion Detection Fang, Weitao Zhang, Tingting Zhao, Chenqiu Soomro, Danyal Badar Taj, Rizwan Hu, Haibo Background subtraction based on random superpixels under multiple scales for video analytics |
| title | Background subtraction based on random superpixels under multiple scales for video analytics |
| title_full | Background subtraction based on random superpixels under multiple scales for video analytics |
| title_fullStr | Background subtraction based on random superpixels under multiple scales for video analytics |
| title_full_unstemmed | Background subtraction based on random superpixels under multiple scales for video analytics |
| title_short | Background subtraction based on random superpixels under multiple scales for video analytics |
| title_sort | background subtraction based on random superpixels under multiple scales for video analytics |
| topic | Computer Vision Motion Detection |
| url | https://hdl.handle.net/10356/87527 http://hdl.handle.net/10220/45446 |
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