# FollowMeUp Sports: New Benchmark for 2D Human Keypoint Recognition

arXiv: https://arxiv.org/abs/1911.08344 · HTML: https://mlsanity.com/paper/1911.08344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Archangel: A Hybrid UAV-based Human Detection Benchmark with Position and Pose Metadata](https://mlsanity.com/paper/2209.00128/) (2022)
- [HitoMi-Cam: A Shape-Agnostic Person Detection Method Using the Spectral Characteristics of Clothing](https://mlsanity.com/paper/2511.08908/) (2025)
- [SAGE-Yoga: Multi-Cue Learning for Yoga Pose Classification and Joint-Level Correction](https://mlsanity.com/paper/2609.20245/) (2026)

## Abstract (arXiv)

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.