TY - GEN
T1 - Estimation of runners’ number of steps, stride length and speed transition from video of a 100-meter race
AU - Yagi, Kentaro
AU - Hasegawa, Kunihiro
AU - Sugiura, Yuta
AU - Saito, Hideo
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2018/10/19
Y1 - 2018/10/19
N2 - The purpose of this study is sensing movements of 100-m runners from video that is publicly available, for example, Internet broadcasts. Normally, information that can be obtained from a video is limited to the number of steps and average stride length. However, our proposed method makes it possible to measure not only this information, but also time-scale information like every stride length and speed transition from the same input. Our proposed method can be divided into three steps. First, we generate a panoramic image of the 100-m track. By doing this, we can estimate where the runners are running in a frame at the 100-meter scale. Second, we detect whether the runner steps in the frame. For this process, we utilize the detected track lines and leg joint positions of runners. Finally, we project every steps to the overview image of the 100-m track to estimate the stride length at the 100-m scale. In the experiment part, we apply our method to various race videos. We evaluate the accuracy of our method via comparison with the data measured using typical methods. In addition, we evaluate the accuracy of estimation of the number of steps and show visualized runners’ steps and speed transitions.
AB - The purpose of this study is sensing movements of 100-m runners from video that is publicly available, for example, Internet broadcasts. Normally, information that can be obtained from a video is limited to the number of steps and average stride length. However, our proposed method makes it possible to measure not only this information, but also time-scale information like every stride length and speed transition from the same input. Our proposed method can be divided into three steps. First, we generate a panoramic image of the 100-m track. By doing this, we can estimate where the runners are running in a frame at the 100-meter scale. Second, we detect whether the runner steps in the frame. For this process, we utilize the detected track lines and leg joint positions of runners. Finally, we project every steps to the overview image of the 100-m track to estimate the stride length at the 100-m scale. In the experiment part, we apply our method to various race videos. We evaluate the accuracy of our method via comparison with the data measured using typical methods. In addition, we evaluate the accuracy of estimation of the number of steps and show visualized runners’ steps and speed transitions.
KW - A 100-m race
KW - Analysis of runners
KW - Image stitching
KW - Track and field
UR - http://www.scopus.com/inward/record.url?scp=85061804317&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061804317&partnerID=8YFLogxK
U2 - 10.1145/3265845.3265850
DO - 10.1145/3265845.3265850
M3 - Conference contribution
AN - SCOPUS:85061804317
T3 - MMSports 2018 - Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports, Co-located with MM 2018
SP - 87
EP - 95
BT - MMSports 2018 - Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports, Co-located with MM 2018
PB - Association for Computing Machinery, Inc
T2 - 1st ACM International Workshop on Multimedia Content Analysis in Sports, ACM 2018, co-located with ACM Multimedia 2018
Y2 - 26 October 2018
ER -