Pose And Joint-Aware Action Recognition

• URL: http://arxiv.org/abs/2010.08164v2
• Date: Fri, 29 Oct 2021 21:12:40 GMT
• Title: Pose And Joint-Aware Action Recognition
• Authors: Anshul Shah, Shlok Mishra, Ankan Bansal, Jun-Cheng Chen, Rama Chellappa, Abhinav Shrivastava
• Abstract summary: We present a new model for joint-based action recognition, which first extracts motion features from each joint separately through a shared motion encoder. Our joint selector module re-weights the joint information to select the most discriminative joints for the task. We show large improvements over the current state-of-the-art joint-based approaches on JHMDB, HMDB, Charades, AVA action recognition datasets.
• Score: 87.4780883700755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent progress on action recognition has mainly focused on RGB and optical flow features. In this paper, we approach the problem of joint-based action recognition. Unlike other modalities, constellation of joints and their motion generate models with succinct human motion information for activity recognition. We present a new model for joint-based action recognition, which first extracts motion features from each joint separately through a shared motion encoder before performing collective reasoning. Our joint selector module re-weights the joint information to select the most discriminative joints for the task. We also propose a novel joint-contrastive loss that pulls together groups of joint features which convey the same action. We strengthen the joint-based representations by using a geometry-aware data augmentation technique which jitters pose heatmaps while retaining the dynamics of the action. We show large improvements over the current state-of-the-art joint-based approaches on JHMDB, HMDB, Charades, AVA action recognition datasets. A late fusion with RGB and Flow-based approaches yields additional improvements. Our model also outperforms the existing baseline on Mimetics, a dataset with out-of-context actions.

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