Papers › Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for...
Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection
Nassim Mokhtari, Alexis Nédélec, Pierre De Loor
Human activity recognition (HAR) based on skeleton data that can be extracted from videos (Kinect for example) , or provided by a depth camera is a time series classification problem, where handling both spatial and temporal dependencies is a crucial task, in order to achieve a good recognition. In the online human activity recognition, identifying the beginning and end of an action is an important element, that might be difficult in a continuous data flow. In this work, we present a 3D skeleton data encoding method to generate an image that preserves the spatial and temporal dependencies existing between the skeletal joints.To allow online action detection we combine this encoding system with a sliding window on the continous data stream. By this way, no start or stop timestamp is needed and the recognition can be done at any moment. A deep learning CNN algorithm is used to achieve actions online detection.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Human Activity Recognition | OAD dataset | STIE + VGG16(fine-tuning) | Accuracy | 86.81 | #3 of 3 | Archive leaderboard | report |
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Methods
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