| Autor: |
Gunasekara, Shanaka Ramesh, Li, Wanqing, Yang, Jack, Ogunbona, Philip |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023 |
| Druh dokumentu: |
Working Paper |
| Popis: |
In skeleton-based human action recognition, temporal pooling is a critical step for capturing spatiotemporal relationship of joint dynamics. Conventional pooling methods overlook the preservation of motion information and treat each frame equally. However, in an action sequence, only a few segments of frames carry discriminative information related to the action. This paper presents a novel Joint Motion Adaptive Temporal Pooling (JMAP) method for improving skeleton-based action recognition. Two variants of JMAP, frame-wise pooling and joint-wise pooling, are introduced. The efficacy of JMAP has been validated through experiments on the popular NTU RGB+D 120 and PKU-MMD datasets. |
| Databáze: |
arXiv |
| Externí odkaz: |
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