Mining Automatically Estimated Poses from Video Recordings of Top Athletes
This work addresses the need for automated performance analysis in professional sports, enabling national associations to extract relevant metrics from abundant video data, though it is incremental in applying existing pose detection to new sports contexts.
The paper tackled the problem of mining noisy, annotation-free pose data from video recordings of top athletes in sports like swimming and long jump, developing algorithms to extract performance metrics such as cycle speeds, striking poses, and phase partitions, with experimental results proving their effectiveness.
Human pose detection systems based on state-of-the-art DNNs are on the go to be extended, adapted and re-trained to fit the application domain of specific sports. Therefore, plenty of noisy pose data will soon be available from videos recorded at a regular and frequent basis. This work is among the first to develop mining algorithms that can mine the expected abundance of noisy and annotation-free pose data from video recordings in individual sports. Using swimming as an example of a sport with dominant cyclic motion, we show how to determine unsupervised time-continuous cycle speeds and temporally striking poses as well as measure unsupervised cycle stability over time. Additionally, we use long jump as an example of a sport with a rigid phase-based motion to present a technique to automatically partition the temporally estimated pose sequences into their respective phases. This enables the extraction of performance relevant, pose-based metrics currently used by national professional sports associations. Experimental results prove the effectiveness of our mining algorithms, which can also be applied to other cycle-based or phase-based types of sport.