Name
Abstract | ||
|---|---|---|
For real-time feedback and cost-efficient analysis from sport videos, it is essential to automatically identify players. In this paper, we propose a method for identifying sport players in videos. Our method uses wearable sensors to obtain their motions. Player identification is achieved by motion feature matching between (unknown) players in videos and wearable sensors whose IDs are already known. We combine three types of motion features, i.e. time sequences of speed, directions and step timings. For step detection from videos, we assume an existing computer vision technique to estimate postures (i.e. 18 joints of a skeleton) of players and design a step detection algorithm. Motion features from wearable sensors are extracted from acceleration, angular velocity and magnetic field. Simulation results show our method successfully identifies 10 players with 72 % accuracy at least even when average posture estimation error is 37.5 (cm). |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICMU.2016.7742086 | 2016 Ninth International Conference on Mobile Computing and Ubiquitous Networking (ICMU) |
Keywords | Field | DocType |
sport videos,wearable sensors,real-time feedback,cost-efficient analysis,sport player identification,motion feature matching,ID,computer vision technique,posture estimation,step detection algorithm,angular velocity,magnetic field,average posture estimation error | Computer vision,Angular velocity,Wearable computer,Computer science,Feature extraction,Feature matching,Step detection,Artificial intelligence,Acceleration,Trajectory | Conference |
ISBN | Citations | PageRank |
978-1-5090-1742-3 | 1 | 0.38 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Hamatani | 1 | 12 | 5.27 |
| Yudai Sakaguchi | 2 | 1 | 0.38 |
| Akira Uchiyama | 3 | 78 | 14.48 |
| Higashino, T. | 4 | 19 | 15.19 |