Crowd estimation using key‐point matching with support vector regression
Abstract The crowd behaviour understanding and density estimation are some of the fast‐growing fields in video surveillance. There are many techniques (detection and regression) that are used as the method of crowd analysis and estimation. In the present approach, SVR (support vector regression) is...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-12-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12300 |
| _version_ | 1798027570112888832 |
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| author | E.M.C.L Ekanayake Yunqi Lei |
| author_facet | E.M.C.L Ekanayake Yunqi Lei |
| author_sort | E.M.C.L Ekanayake |
| collection | DOAJ |
| description | Abstract The crowd behaviour understanding and density estimation are some of the fast‐growing fields in video surveillance. There are many techniques (detection and regression) that are used as the method of crowd analysis and estimation. In the present approach, SVR (support vector regression) is used as the basic analysis technique and the novel key‐point matching with SURF (speedup robust feature) is used as the feature extractor for moving objects in the video. The traditional linear regression methods used mainly key‐point as one of the statistical features instead of matching with consecutive frames, but we used the magnitude of the optical flow for foreground object extraction instead of inter‐frame difference. The combination of the optical flow of foreground objects and key‐point matching generates new features apart from conventional features such as areas and corners. In this new approach, key‐point pairing with linear regression is tested with the PETS2009 dataset, and performance is compared with the existing approaches. |
| first_indexed | 2024-04-11T18:53:44Z |
| format | Article |
| id | doaj.art-0590108273ae48ba83ad4b95eb10047d |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-11T18:53:44Z |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-0590108273ae48ba83ad4b95eb10047d2022-12-22T04:08:14ZengWileyIET Image Processing1751-96591751-96672021-12-0115143551355810.1049/ipr2.12300Crowd estimation using key‐point matching with support vector regressionE.M.C.L Ekanayake0Yunqi Lei1Department of Computer Science School of Information Science and Engineering Xiamen University Xiamen ChinaDepartment of Computer Science School of Information Science and Engineering Xiamen University Xiamen ChinaAbstract The crowd behaviour understanding and density estimation are some of the fast‐growing fields in video surveillance. There are many techniques (detection and regression) that are used as the method of crowd analysis and estimation. In the present approach, SVR (support vector regression) is used as the basic analysis technique and the novel key‐point matching with SURF (speedup robust feature) is used as the feature extractor for moving objects in the video. The traditional linear regression methods used mainly key‐point as one of the statistical features instead of matching with consecutive frames, but we used the magnitude of the optical flow for foreground object extraction instead of inter‐frame difference. The combination of the optical flow of foreground objects and key‐point matching generates new features apart from conventional features such as areas and corners. In this new approach, key‐point pairing with linear regression is tested with the PETS2009 dataset, and performance is compared with the existing approaches.https://doi.org/10.1049/ipr2.12300Image recognitionComputer vision and image processing techniquesVideo signal processingRegression analysisRegression analysis |
| spellingShingle | E.M.C.L Ekanayake Yunqi Lei Crowd estimation using key‐point matching with support vector regression IET Image Processing Image recognition Computer vision and image processing techniques Video signal processing Regression analysis Regression analysis |
| title | Crowd estimation using key‐point matching with support vector regression |
| title_full | Crowd estimation using key‐point matching with support vector regression |
| title_fullStr | Crowd estimation using key‐point matching with support vector regression |
| title_full_unstemmed | Crowd estimation using key‐point matching with support vector regression |
| title_short | Crowd estimation using key‐point matching with support vector regression |
| title_sort | crowd estimation using key point matching with support vector regression |
| topic | Image recognition Computer vision and image processing techniques Video signal processing Regression analysis Regression analysis |
| url | https://doi.org/10.1049/ipr2.12300 |
| work_keys_str_mv | AT emclekanayake crowdestimationusingkeypointmatchingwithsupportvectorregression AT yunqilei crowdestimationusingkeypointmatchingwithsupportvectorregression |