Publication details
SPEED21: Speed Climbing Motion Dataset
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | MMSports'21: Proceedings of the 4th International Workshop on Multimedia Content Analysis in Sports |
| MU Faculty or unit | |
| Citation | |
| Web | https://dl.acm.org/doi/10.1145/3475722.3482795 |
| Doi | http://dx.doi.org/10.1145/3475722.3482795 |
| Keywords | speed climbing; sports dataset; 2d skeleton series; k-nn search; similarity |
| Description | With the recent advances in computer vision and deep learning, the research interest in video-based and skeleton-based sports analysis is growing. Also, speed climbing as a sport is on the rise, being included as an Olympic sport in Tokyo 2020. This work aims to connect both of these worlds. First, a dataset of 362 speed climbing performances is provided for the community of domain experts and practitioners in human motion understanding and sports analysis. The dataset annotates pre-segmented performances of 55 world elite athletes in the form of 2D skeleton sequences extracted from world competition events videos. Secondly, a high descriptiveness and usability of 2D skeleton data is demonstrated in the search scenario that matches climbers by the similarities in their climbing style with high accuracy. The high k-NN search precision above 90 % is achieved by a synergic combination of suitable representation with a semi-dependent variant of Dynamic Time Warping (DTW). The proposed DTW variant computes distances separately across individual semantic body parts (e.g., hands and feet) whose atoms (joints or angles) are wired together for the temporal alignment. |
| Related projects: |