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Summary of Egoexo-fitness: Towards Egocentric and Exocentric Full-body Action Understanding, by Yuan-ming Li et al.


EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding

by Yuan-Ming Li, Wei-Jin Huang, An-Lan Wang, Ling-An Zeng, Jing-Ke Meng, Wei-Shi Zheng

First submitted to arxiv on: 13 Jun 2024

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: Artificial Intelligence (cs.AI)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper presents EgoExo-Fitness, a novel dataset for full-body action understanding, featuring fitness videos recorded from both egocentric (first-person) and exocentric (third-person) cameras. The dataset provides rich annotations, including two-level temporal boundaries, sub-step localization, technical keypoint verification, natural language comments on action execution, and quality scores. EgoExo-Fitness aims to study full-body action understanding across dimensions of “what”, “when”, and “how well”. To facilitate research, the authors construct benchmarks for various tasks, including action classification, localization, sequence verification, skill determination, and guidance-based execution verification.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper creates a new dataset called EgoExo-Fitness that helps computers understand people doing different physical activities from different camera angles. The data includes videos recorded from both what’s happening in front of the camera (first-person) and from outside looking at the person (third-person). This information is helpful for tasks like recognizing actions, detecting when something starts or ends, and checking if someone is doing an activity correctly. The researchers also made some tools to help people do research on this topic.

Keywords

» Artificial intelligence  » Classification