FollowMeUp Sports: New Benchmark for 2D Human Keypoint Recognition
This provides a domain-specific dataset for human pose estimation, particularly in sports and workout activities, which is incremental as it builds on existing datasets by improving coverage.
The authors introduced FollowMeUp Sports, a new benchmark dataset for 2D human keypoint recognition that addresses limitations in pose variety, self-occlusion, and class balance, and they analyzed leading pose estimation methods to identify their strengths and weaknesses.
Human pose estimation has made significant advancement in recent years. However, the existing datasets are limited in their coverage of pose variety. In this paper, we introduce a novel benchmark FollowMeUp Sports that makes an important advance in terms of specific postures, self-occlusion and class balance, a contribution that we feel is required for future development in human body models. This comprehensive dataset was collected using an established taxonomy of over 200 standard workout activities with three different shot angles. The collected videos cover a wider variety of specific workout activities than previous datasets including push-up, squat and body moving near the ground with severe self-occlusion or occluded by some sport equipment and outfits. Given these rich images, we perform a detailed analysis of the leading human pose estimation approaches gaining insights for the success and failures of these methods.