Name
Abstract | ||
|---|---|---|
Hand tracking is becoming more and more popular in the field of human-computer interaction (HCI). A lot of studies in this area have made good progress. However, robust hand tracking is still difficult in long-term. On-line learning technology has great potential in terms of tracking for its strong adaptive learning ability. To address the problem we combined an on-line learning technology called on-line boosting with an off-line trained detector to track the hand. The contributions of this paper are: 1) we propose a learning method with an off-line model to solve the drift of on-line learning; 2) we build a framework for hand tracking based on the learning method. The experiments show that compared with other three methods, the proposed tracker is more robust in the strain case. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2197034 | SEVENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2015) |
Keywords | Field | DocType |
Hand tracking,human-computer interaction,on-line boosting,AdaBoost | Educational technology,Computer vision,Off line,AdaBoost,Computer science,Boosting (machine learning),Artificial intelligence,Detector,Adaptive learning,Machine learning | Conference |
Volume | ISSN | ISBN |
9631 | 0277-786X | 978-1-62841-829-3 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Jiangyue | 1 | 0 | 0.34 |
| Zhao Yong | 2 | 90 | 14.85 |
| Liang Hao | 3 | 0 | 1.69 |
| Cheng Ruzhong | 4 | 6 | 1.15 |
| Wei Yiqun | 5 | 1 | 1.70 |