line/Skeleton-Temporal-Action-Localization
Official implementation of AAAI 2023 Oral Paper "Frame-Level Label Refinement for Skeleton-Based Weakly-Supervised Action Recognition"
This project helps researchers and developers working with human motion data to automatically identify specific actions within long, unlabeled skeleton-based video recordings. You input raw human motion capture data (like AMASS and BABEL datasets) and it outputs precise, frame-level labels indicating when particular actions start and end. This is for machine learning researchers and computer vision engineers developing human activity recognition systems.
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Use this if you need to precisely locate and label human actions in skeleton-based motion data for machine learning tasks, even with only high-level activity descriptions.
Not ideal if you are looking for a pre-trained, ready-to-use application for action recognition without extensive setup and dataset preparation.
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Language
Python
License
Apache-2.0
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Last pushed
Oct 20, 2023
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