The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Recognizing Human Actions as the Evolution of Pose Estimation Maps
Open accessAbstract
Most video-based action recognition approaches choose to extract features from the whole video to recognize actions. The cluttered background and non-action motions limit the performances of these methods, since they lack the explicit modeling of human body movements. With recent advances of human pose estimation, this work presents a novel method to recognize human action as the evolution of pose estimation maps. Instead of relying on the inaccurate human poses estimated from videos, we observe that pose estimation maps, the byproduct of pose estimation, preserve richer cues of human body to benefit action recognition. Specifically, the evolution of pose estimation maps can be decomposed as an evolution of heatmaps, e.g., probabilistic maps, and an evolution of estimated 2D human poses, which denote the changes of body shape and body pose, respectively. Considering the sparse property of heatmap, we develop spatial rank pooling to aggregate the evolution of heatmaps as a body shape evolution image. As body shape evolution image does not differentiate body parts, we design body guided sampling to aggregate the evolution of poses as a body pose evolution image. The complementary properties between both types of images are explored by deep convolutional neural networks to predict action label. Experiments on NTU RGB+D, UTD-MHAD and PennAction datasets verify the effectiveness of our method, which outperforms most state-of-the-art methods.
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Cites
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Two-Stream Convolutional Networks for Action Recognition in Videos
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NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
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Real-time human pose recognition in parts from single depth images
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Cited by
Skeleton-based action recognition via spatial and temporal transformer networks
Plizzari, C., Cannici, M., & Matteucci, M. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103219 · Cited by 409
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
Cheng, K., Zhang, Y., Cao, C., Shi, L., Cheng, J., & Lu, H. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58586-0_32 · Cited by 384
Skeleton Image Representation for 3D Action Recognition Based on Tree Structure and Reference Joints
Caetano, C., Bremond, F., & Schwartz, W. R. (2019) · 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2019.00011 · Cited by 149
Learning Multi-View Interactional Skeleton Graph for Action Recognition
Wang, M., Ni, B., & Yang, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3032738 · Cited by 67
Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction
Zhang, W., Zhao, S., Meng, F., Wu, S., & Liu, M. (2023) · Proceedings of the 31st ACM International Conference on Multimedia · doi:10.1145/3581783.3612532 · Cited by 10
Self-citation
Metadata
- Authors
- Liu, M., & Yuan, J.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1159-1168
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2799211965
- Cited by
- 336
- References
- 55
- FWCI
- 12.85
- Type
- conference paper
- Language
- en
- Open access
- green
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