FollowMeUp Sports: New Benchmark for 2D Human Keypoint Recognition

TL;DR

FollowMeUp Sports is a new comprehensive benchmark dataset for 2D human keypoint recognition, featuring diverse workout activities, challenging poses, and occlusions to advance pose estimation research.

Contribution

The paper introduces FollowMeUp Sports, a novel dataset with extensive pose variety, occlusion, and class balance, addressing limitations of existing datasets for human pose estimation.

Findings

Analysis of leading pose estimation methods

Insights into success factors and failure modes

Benchmark results on the new dataset

Abstract

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.