Abstract
This paper proposes a method for reconstructing 3D ball trajectories by using multiple temporally and geometrically uncalibrated cameras. To use cameras to measure the trajectory of a fast-moving object, such as a ball thrown by a pitcher, the cameras must be temporally synchronized and their position and orientation should be calibrated. In some cases, these conditions cannot be met, e.g., one cannot geometrically calibrate cameras when one cannot step into a baseball stadium. The basic idea of the proposed method is to use a ball captured by multiple cameras as a corresponding point. The method first detects a ball. Then, it estimates temporal difference between cameras. After that, the ball positions are used as corresponding points for geometrically calibrating the cameras. Experiments using actual pitching videos verify the effectiveness of our method.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 |
| Publisher | IEEE Computer Society |
| Pages | 164-169 |
| Number of pages | 6 |
| Volume | 2017-July |
| ISBN (Electronic) | 9781538607336 |
| DOIs | |
| Publication status | Published - 2017 Aug 22 |
| Event | 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 - Honolulu, United States Duration: 2017 Jul 21 → 2017 Jul 26 |
Other
| Other | 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 17/7/21 → 17/7/26 |
Fingerprint
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
Cite this
Ball 3D Trajectory Reconstruction without Preliminary Temporal and Geometrical Camera Calibration. / Miyata, Shogo; Saito, Hideo; Takahashi, Kosuke; Mikami, Dan; Isogawa, Mariko; Kimata, Hideaki.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017. Vol. 2017-July IEEE Computer Society, 2017. p. 164-169 8014760.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Ball 3D Trajectory Reconstruction without Preliminary Temporal and Geometrical Camera Calibration
AU - Miyata, Shogo
AU - Saito, Hideo
AU - Takahashi, Kosuke
AU - Mikami, Dan
AU - Isogawa, Mariko
AU - Kimata, Hideaki
PY - 2017/8/22
Y1 - 2017/8/22
N2 - This paper proposes a method for reconstructing 3D ball trajectories by using multiple temporally and geometrically uncalibrated cameras. To use cameras to measure the trajectory of a fast-moving object, such as a ball thrown by a pitcher, the cameras must be temporally synchronized and their position and orientation should be calibrated. In some cases, these conditions cannot be met, e.g., one cannot geometrically calibrate cameras when one cannot step into a baseball stadium. The basic idea of the proposed method is to use a ball captured by multiple cameras as a corresponding point. The method first detects a ball. Then, it estimates temporal difference between cameras. After that, the ball positions are used as corresponding points for geometrically calibrating the cameras. Experiments using actual pitching videos verify the effectiveness of our method.
AB - This paper proposes a method for reconstructing 3D ball trajectories by using multiple temporally and geometrically uncalibrated cameras. To use cameras to measure the trajectory of a fast-moving object, such as a ball thrown by a pitcher, the cameras must be temporally synchronized and their position and orientation should be calibrated. In some cases, these conditions cannot be met, e.g., one cannot geometrically calibrate cameras when one cannot step into a baseball stadium. The basic idea of the proposed method is to use a ball captured by multiple cameras as a corresponding point. The method first detects a ball. Then, it estimates temporal difference between cameras. After that, the ball positions are used as corresponding points for geometrically calibrating the cameras. Experiments using actual pitching videos verify the effectiveness of our method.
UR - http://www.scopus.com/inward/record.url?scp=85030228158&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030228158&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2017.26
DO - 10.1109/CVPRW.2017.26
M3 - Conference contribution
AN - SCOPUS:85030228158
VL - 2017-July
SP - 164
EP - 169
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
PB - IEEE Computer Society
ER -