An improved corner detection algorithm used in video statistics
In order to address the difficult problem to determine the number of populations, this paper improves the algorithm based on the Harris point detection algorithm, and the number of people is returned through the first-order linear regression model. First of all, according to the shortcomings of Harr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2018-11-01
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| Series: | Journal of Algorithms & Computational Technology |
| Online Access: | https://doi.org/10.1177/1748301818813485 |
| _version_ | 1818313424770170880 |
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| author | Sun Wanchun Zhang Jianxun |
| author_facet | Sun Wanchun Zhang Jianxun |
| author_sort | Sun Wanchun |
| collection | DOAJ |
| description | In order to address the difficult problem to determine the number of populations, this paper improves the algorithm based on the Harris point detection algorithm, and the number of people is returned through the first-order linear regression model. First of all, according to the shortcomings of Harris corner algorithm in population statistics, an adaptive gray difference idea is proposed, and the concept of integral image is introduced to overcome its defects in noise immunity and real-time operation. Secondly, in view of the large error generated in the process of population statistics in the first-order static model, a dynamic linear model regression method is proposed. In this method, it is believed that there is certain proportionality coefficient between each frame of corner points and the number of people with the change of time, and this coefficient has certain correlation with the angle points in the previous frame and current frame. At the same time, in order to eliminate the number of redundant corners generated in the corner statistics process, the frame difference method is used to filter the stationary point. Finally, the number of people is returned through first-order linear model. |
| first_indexed | 2024-12-13T08:33:32Z |
| format | Article |
| id | doaj.art-5916b83b8efc4a64a797568f481f40fb |
| institution | Directory Open Access Journal |
| issn | 1748-3026 |
| language | English |
| last_indexed | 2024-12-13T08:33:32Z |
| publishDate | 2018-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Algorithms & Computational Technology |
| spelling | doaj.art-5916b83b8efc4a64a797568f481f40fb2022-12-21T23:53:42ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30262018-11-011310.1177/1748301818813485An improved corner detection algorithm used in video statisticsSun WanchunZhang JianxunIn order to address the difficult problem to determine the number of populations, this paper improves the algorithm based on the Harris point detection algorithm, and the number of people is returned through the first-order linear regression model. First of all, according to the shortcomings of Harris corner algorithm in population statistics, an adaptive gray difference idea is proposed, and the concept of integral image is introduced to overcome its defects in noise immunity and real-time operation. Secondly, in view of the large error generated in the process of population statistics in the first-order static model, a dynamic linear model regression method is proposed. In this method, it is believed that there is certain proportionality coefficient between each frame of corner points and the number of people with the change of time, and this coefficient has certain correlation with the angle points in the previous frame and current frame. At the same time, in order to eliminate the number of redundant corners generated in the corner statistics process, the frame difference method is used to filter the stationary point. Finally, the number of people is returned through first-order linear model.https://doi.org/10.1177/1748301818813485 |
| spellingShingle | Sun Wanchun Zhang Jianxun An improved corner detection algorithm used in video statistics Journal of Algorithms & Computational Technology |
| title | An improved corner detection algorithm used in video statistics |
| title_full | An improved corner detection algorithm used in video statistics |
| title_fullStr | An improved corner detection algorithm used in video statistics |
| title_full_unstemmed | An improved corner detection algorithm used in video statistics |
| title_short | An improved corner detection algorithm used in video statistics |
| title_sort | improved corner detection algorithm used in video statistics |
| url | https://doi.org/10.1177/1748301818813485 |
| work_keys_str_mv | AT sunwanchun animprovedcornerdetectionalgorithmusedinvideostatistics AT zhangjianxun animprovedcornerdetectionalgorithmusedinvideostatistics AT sunwanchun improvedcornerdetectionalgorithmusedinvideostatistics AT zhangjianxun improvedcornerdetectionalgorithmusedinvideostatistics |