Fusing multi-modal features for gesture recognition

By Jiaxiang Wu, Jian Cheng, Chaoyang Zhao, Hanqing Lu. 2013

72 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Developmental and Educational Psychology, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. G. Wang
2. Jun Liu
3. Liang Wang
4. Luc Van Gool
5. Dacheng Tao
6. Ming-Hsuan Yang
7. Andrew Zisserman
8. Yi Yang

In Developmental and Educational Psychology

1. Liang Wang
2. Jun Liu
3. Ling Shao
4. Di Wu
5. Hongsong Wang
6. Yun Raymond Fu

In Human-Computer Interaction

1. Liang Wang
2. Hongsong Wang
3. Jun Liu
4. Yun Raymond Fu
5. Yong Du
6. Alex ChiChung Kot
7. Amir Shahroudy
8. G. Wang

Citations in the corpus

• Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates (2017, methodology): “As shown in Table 8, our method surpasses the state-ofthe-art methods [39], [91], [92], [93], [94], [95], which demonstrates the effectiveness of our method in dealing with skeleton-based action recognition problem.”
• Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks (2017, methodology): “Precision Recall F1-score Skeleton Feature [48] 59.9 59.3 59.6 Portfolios [52] – – 56.0 Gesture Spotting [35] 61.2 62.3 61.7 HiVideoDarwin [45] 74.9 75.6 74.6 CNN for Skeleton [5] 91”
• Domain-Adaptive Discriminative One-Shot Learning of Gestures (2014, methodology): “5929 (higher is better) using skeleton features for classification [34].”
• Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition (2014, methodology): “lenge [29] that uses variant of nearest neighbour and dynamic time”
• Rank Pooling for Action Recognition (2015, methodology): “on accuracy. information explicitly. Precision Recall F-score Rank pooling 75.3 75.1 75.2 Martinez-Camarena et al [40] 61.4 61.9 61.6 Pfister et al [45] 61.2 62.3 61.7 Yao et al [73] – – 56.0 Wu et al [71] 59.9 59.3 59.6 Table 10: Comparison of the proposed approach with the state-of-the-art methods ”
• Hierarchical motion evolution for action recognition (2015, unknown): “[26] 59.”
• Skeleton based action recognition with convolutional neural network (2015, methodology): “We follow the experimental protocol adopted in [3, 14, 22, 25] and provide precision, recall and F1-score measures on the validation set.”
• Modeling video evolution for action recognition (2015, unknown): “[41] 59.”