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arXiv · 2305.08191

Is end-to-end learning enough for fitness activity recognition?

Antoine Mercier·Guillaume Berger·Sunny Panchal·Florian Letsch·Cornelius Boehm·Nahua Kang·Ingo Bax·Roland Memisevic
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Abstract

End-to-end learning has taken hold of many computer vision tasks, in particular, related to still images, with task-specific optimization yielding very strong performance. Nevertheless, human-centric action recognition is still largely dominated by hand-crafted pipelines, and only individual components are replaced by neural networks that typically operate on individual frames. As a testbed to study the relevance of such pipelines, we present a new fully annotated video dataset of fitness activities. Any recognition capabilities in this domain are almost exclusively a function of human poses and their temporal dynamics, so pose-based solutions should perform well. We show that, with this labelled data, end-to-end learning on raw pixels can compete with state-of-the-art action recognition pipelines based on pose estimation. We also show that end-to-end learning can support temporally fine-grained tasks such as real-time repetition counting.

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Antoine Mercier, Guillaume Berger, Sunny Panchal, Florian Letsch, Cornelius Boehm, Nahua Kang, Ingo Bax, Roland Memisevic. 2023-05-14. Is end-to-end learning enough for fitness activity recognition?. https://arxiv.org/abs/2305.08191

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