The design of fall detection algorithm based on multi-feature analysis
In view of the shortcomings of high detection error rate of the existing fall detection algorithm,an improved fall detection algorithm is proposed.First,the Gaussian mixture model is used to detect the foreground object,and then median filtering and morphological processing are used to extract the f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Academic Journals Center of Shanghai Normal University
2017-04-01
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| Series: | Journal of Shanghai Normal University (Natural Sciences) |
| Subjects: | |
| Online Access: | http://qktg.shnu.edu.cn/zrb/shsfqkszrb/ch/reader/view_abstract.aspx?file_no=20180218&flag=1 |
| Summary: | In view of the shortcomings of high detection error rate of the existing fall detection algorithm,an improved fall detection algorithm is proposed.First,the Gaussian mixture model is used to detect the foreground object,and then median filtering and morphological processing are used to extract the foreground object.Based on the use of human aspect ratio and effective area ratio,the change of centroid,orientation angle and motion coefficient are taken as features to judge whether the human has fallen.Compared with traditional algorithnal,experimental results show that the proposed algorithm has higher accuracy,higher sensitivity,low algorithm complexity,and can effectively prevent misjudgment. |
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| ISSN: | 1000-5137 1000-5137 |