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Application-Driven AI Paradigm for Human Action Recognition

arXiv 2209.15271 ↗First posted 30 Sep 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

Human action recognition in computer vision has been widely studied in recent years. However, most algorithms consider only certain action specially with even high computational cost. That is not suitable for practical applications with multiple actions to be identified with low computational cost. To meet various application scenarios, this paper presents a unified human action recognition framework composed of two modules, i.e., multi-form human detection and corresponding action classification. Among them, an open-source dataset is constructed to train a multi-form human detection model that distinguishes a human being's whole body, upper body or part body, and the followed action classification model is adopted to recognize such action as falling, sleeping or on-duty, etc. Some experimental results show that the unified framework is effective for various application scenarios. It is expected to be a new application-driven AI paradigm for human action recognition.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 66 references that are in the map. First posted on arXiv 30 Sep 2022.

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30 of 66 references.

References · 30

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