Name
Abstract | ||
|---|---|---|
Hand detection is essential for many hand related tasks, e.g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction. However, hand detection in uncontrolled environments is challenging due to the flexibility of wrist joint and cluttered background. We propose a deep learning based approach which detects hands and calibrates in-plane rotation under supervision at the same time. To guarantee the recall, we propose a context aware proposal generation algorithm which significantly outperforms the selective search. We then design a convolutional neural network(CNN) which handles object rotation explicitly to jointly solve the object detection and rotation estimation tasks. Experiments show that our method achieves better results than state-of-the-art detection models on widely-used benchmarks such as Oxford and Egohands database. We further show that rotation estimation and classification can mutually benefit each other. |
Year | Venue | DocType |
|---|---|---|
2016 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1612.02742 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoming Deng | 1 | 68 | 7.59 |
| Ye Yuan | 2 | 25 | 9.72 |
| Yinda Zhang | 3 | 0 | 0.34 |
| Ping Tan | 4 | 0 | 2.37 |
| Liang Chang | 5 | 0 | 3.38 |
| Shuo Yang | 6 | 59 | 8.76 |
| Hongan Wang | 7 | 642 | 79.77 |