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2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning
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Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we propose a multitask framework for jointly 2D and 3D pose estimation from still images and human action recognition from video sequences. We show that a single architecture can be used to solve the two problems in an efficient way and still achieves state-of-the-art results. Additionally, we demonstrate that optimization from end-to-end leads to significantly higher accuracy than separated learning. The proposed architecture can be trained with data from different categories simultaneously in a seamlessly way. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the effectiveness of our method on the targeted tasks.
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Metadata
- Authors
- Luvizon, D. C., Picard, D., & Tabia, H.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5137-5146
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2788865504
- Cited by
- 555
- References
- 61
- FWCI
- 22.38
- Type
- conference paper
- Language
- en
- Open access
- green
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