Name
Abstract | ||
|---|---|---|
Over the past 2 decades, vision-based dynamic hand gesture recognition (HGR) has made significant progresses and been widely adopted in many practical applications. Although the advent of RGB-D cameras and deep learning-based methods provides more feasible solutions for HGR, it is still very challenging to satisfy the requirements of both high efficiency and accuracy for real-world HGR systems. In this paper, we propose a novel method using the feature covariance matrix for effective and efficient dynamic HGR. We extract a set of local feature vectors that represent local motion patterns to construct the feature covariance matrix efficiently, which also provides a compact representation of a dynamic hand gesture. By tracking hand keypoints in three successive frames and calculating their motion features, our method can be extended to both 2D dynamic HGR and 3D dynamic HGR. To evaluate the effectiveness of the proposed framework, we perform extensive experiments on three publicly available datasets (one 2D dataset and two 3D datasets). The experimental results demonstrate the effectiveness of our proposed method.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00521-018-3719-3 | Neural Computing and Applications |
Keywords | Field | DocType |
Dynamic hand gesture recognition, Feature covariance matrix, Pyramid Lucas–Kanade tracker, Temporal hierarchical construction | Feature vector,Pattern recognition,Gesture,Gesture recognition,RGB color model,Artificial intelligence,Covariance matrix,Deep learning,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
31.0 | 12 | 1433-3058 |
Citations | PageRank | References |
1 | 0.38 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linpu Fang | 1 | 3 | 2.47 |
| Guile Wu | 2 | 17 | 2.39 |
| Wenxiong Kang | 3 | 38 | 6.66 |
| Qiuxia Wu | 4 | 9 | 3.20 |
| Zhiyong Wang | 5 | 550 | 51.76 |
| David Dagan Feng | 6 | 3329 | 413.76 |