Due to the increasing capabilities of computer vision methods, it is now possible to apply them even to the most difficult scenarios, such as for vision-based analysis of a jiu-jitsu match. One of the biggest challenges of such scenarios are heavily occluded scenes. Jiu-jitsu is a grappling martial art in which athletes are interlocked in complex positions most of the time, which produces significant challenges. We propose a method to track the athletes’ poses even in such scenarios. The advantage of our method is that it combines positional, structural, and visual cues to overcome this problem and is able to cope with severe occlusions. We use this data to automatically predict combat positions at a high accuracy. Finally, we propose a novel approach for automatic scoring of a jiu-jitsu match from video using these predictions.
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