Name
Abstract | ||
|---|---|---|
Human motion is a critical aspect of interacting, even between people. It has become an interesting field to exploit in human-robot interaction. Even with today's computing power, it remains a difficult task to successfully follow the human's motion from image processing alone. New sensors were introduced, bringing depth sensing at low or no cost. Using this new technology, this paper presents a new methodology to see space with multiple depth sensors, using machine-learning technique, and features in voxel space to learn to reconstruct humans' joints in single, fused acquisitions. We back up and validate the procedure with ground truth acquired from commercial Motion Capture, and prove the approach to perform particularly well on an expansive set of motion and poses, and compare with current standard software on single depth sensors. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SMC.2013.153 | SMC |
Keywords | Field | DocType |
human motion,critical aspect,new methodology,single depth sensor,new technology,human motion capture,voxel space,multiple depth data,computing power,new sensor,multiple depth sensor,commercial motion capture,machine learning,sensor fusion,voxel,learning artificial intelligence,image reconstruction,image sensors | Iterative reconstruction,Computer vision,Motion capture,Image sensor,Computer science,Image processing,Sensor fusion,Software,Ground truth,Artificial intelligence,3D reconstruction | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wassim Filali | 1 | 4 | 1.66 |
| Jean-Thomas Masse | 2 | 1 | 0.70 |
| Frederic Lerasle | 3 | 23 | 3.52 |
| Jean-Louis Boizard | 4 | 21 | 3.72 |
| Michel Devy | 5 | 542 | 71.47 |