Multi-view hand detection applying viola-jones framework using SAMME AdaBoost
© 2015 IEEE. Human hand detection is one of a popular researches in the field of object detection. One obvious problem of hand detection is about orientation angles of the hand position. That is, most detectors cannot detect a human hand lying in various orientation angles recently. Detecting hand w...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2015
|
| Online Access: | http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84925849052&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38914 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Chiang Mai University |
| Summary: | © 2015 IEEE. Human hand detection is one of a popular researches in the field of object detection. One obvious problem of hand detection is about orientation angles of the hand position. That is, most detectors cannot detect a human hand lying in various orientation angles recently. Detecting hand with various orientation angles can be done using decision tree as a degree estimator. Using the decision tree as a degree estimator can cause the over-fit problem. In this paper, we propose the use of SAMME algorithm instead of the decision tree to prevent the problem. Moreover, from our experimental results, using SAMME as the degree estimator provides detection rate not less than using decision tree as the degree estimator. The results obtained from using SAMME algorithm as the degree estimator show that our detection rates increase by 4.01% (from 78.71 to 82.72) and 8.75% (from 77.82 to 86.57) on two experimental datasets. Their false positive rates decrease from 1 out of 2,959 to 1 out of 3,805 in the first dataset and from 1 out of 2,663 to 1 out of 4,566 in the second dataset, both of which are very low. |
|---|