Joint Temporal Pooling for Improving Skeleton-based Action Recognition

• URL: http://arxiv.org/abs/2408.09356v1
• Date: Sun, 18 Aug 2024 04:40:16 GMT
• Title: Joint Temporal Pooling for Improving Skeleton-based Action Recognition
• Authors: Shanaka Ramesh Gunasekara, Wanqing Li, Jack Yang, Philip Ogunbona,
• Abstract summary: In skeleton-based human action recognition, temporal pooling is a critical step for capturing relationship of joint dynamics. This paper presents a novel Adaptive Joint Motion Temporal Pooling (MAP) method for improving skeleton-based action recognition. The efficacy of JMAP has been validated through experiments on the popular NTU RGBD+ 120 and PKU-MMD datasets.
• Score: 4.891381363264954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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