Name
Title | ||
|---|---|---|
Use of ropes histograms as joints trajectories representation for human motion recognition |
Abstract | ||
|---|---|---|
In this paper, a new approach for 3D skeleton-based human motion recognition is discussed. First, we opted to represent the movement as a set of body joints trajectories. Those trajectories are then converted into ropes histograms. The motion records are obtained using the Kinect motion sensor. The classification phase consists in comparing those histograms with ropes histograms of a set of reference motions. This method is then tested on a random dataset of recorded motions and have presented an accuracy rate of 85%. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ATSIP.2017.8075532 | 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) |
Keywords | Field | DocType |
Human motion recognition,skeleton,joints,ropes histograms | Histogram,Computer vision,Computer science,Human motion,Motion sensors,Artificial intelligence,Hidden Markov model,Trajectory,Body joints | Conference |
ISBN | Citations | PageRank |
978-1-5386-0552-3 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeineb Nejim | 1 | 0 | 0.34 |
| Makrem Mestiri | 2 | 0 | 0.34 |
| Hamid Amiri | 3 | 86 | 19.36 |