The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
On Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks
Abstract
RNN-based approaches have achieved outstanding performance on action recognition with skeleton inputs. Currently these methods limit their inputs to coordinates of joints and improve the accuracy mainly by extending RNN models to spatial domains in various ways. While such models explore relations between different parts directly from joint coordinates, we provide a simple universal spatial modeling method perpendicular to the RNN model enhancement. Specifically, we select a set of simple geometric features, motivated by the evolution of previous work. With experiments on a 3-layer LSTM framework, we observe that the geometric relational features based on distances between joints and selected lines outperform other features and achieve state-of-art results on four datasets. Further, we show the sparsity of input gate weights in the first LSTM layer trained by geometric features and demonstrate that utilizing joint-line distances as input require less data for training.
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Cited by
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12328 · Cited by 5,410
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00572 · Cited by 972
Human Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 601
Video Question Answering via Gradually Refined Attention over Appearance and Motion
Xu, D., Zhao, Z., Xiao, J., Wu, F., Zhang, H., He, X., & Zhuang, Y. (2017) · Proceedings of the 25th ACM international conference on Multimedia · doi:10.1145/3123266.3123427 · Cited by 429
Self-citationGraph Distillation for Action Detection with Privileged Modalities
Luo, Z., Hsieh, J.-T., Jiang, L., Niebles, J. C., & Fei-Fei, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_11 · Cited by 111
Metadata
- Authors
- Zhang, S., Liu, X., & Xiao, J.
- Venue
- 2017 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 148-157
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2612707971
- Cited by
- 310
- References
- 4
- FWCI
- 13.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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