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...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/0590108273ae48ba83ad4b95eb10047d |
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| Sumario: | 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. |
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