Name
Abstract | ||
|---|---|---|
We propose the DeepPose-based pose estimation system that is flexible with the change of bounding-box range for top-view images. Our purpose is to link person detection system and pose estimation system. We introduce Bounding-box Curriculum Learning (BCL) and Recurrent Pose Estimation (RPE). BCL is a learning technique of CNN inspired from Curriculum Learning. RPE is a recurrent process of pose estimation that fixes the bounding-box range in response to the estimated results. We show the effect of proposed methods compared to normal learned CNN-based pose estimator on our original top-view dataset. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE 5TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS | Pose Estimation, Convolutional Neural Networks, Top-view |
Field | DocType | Citations |
Computer vision,Convolutional neural network,Computer science,3D pose estimation,Pose,Person detection,Artificial intelligence,Estimator | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryuji Go | 1 | 0 | 0.34 |
| Yoshimitsu Aoki | 2 | 80 | 23.65 |