Robust human action recognition via long short-term memory

By Alexander Grushin, Derek Monner, James A. Reggia, Ajay Kumar Mishra. 2013

77 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, 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 Biomedical Engineering

1. Liang Wang
2. Tien-Ping Tan
3. Wanqing Li
4. Yongzhen Huang
5. Yong Du
6. Xiaogang Wang
7. Jun Liu
8. Pichao Wang

In Computer Vision and Pattern Recognition

1. Liang Wang
2. Dacheng Tao
3. Xiaogang Wang
4. Luc Van Gool
5. Yi Yang
6. Tien-Ping Tan
7. G. Wang
8. Jun Liu

In Human-Computer Interaction

1. Tien-Ping Tan
2. Liang Wang
3. Dima Damen
4. Yongzhen Huang
5. Majid Mirmehdi
6. Yong Du
7. Naifan Zhuang
8. Guo-Jun Qi

Citations in the corpus

• Differential Recurrent Neural Networks for Action Recognition (2015, background): “Recently, Recurrent Neural Networks (RNNs) [28], especially Long Short-Term Memory (LSTM) model [14], have gained significant attention in solving many challenging problems involving time-series data, such as action recognition [12, 8, 13], multilingual machine translation [30, 4], multimodal transl”
• Detecting the Moment of Completion: Temporal Models for Localising Action Completion (2017, background): “In many recent works, LSTMs have achieved promising results for the task of action recognition/detection, such as [26,18,27,28,2,3].”
• Hierarchical recurrent neural network for skeleton based action recognition (2015, methodology): “[11] use LSTM-RNN for robust action recognition and achieve good results on KTH dataset.”
• A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs (2017, background): “Recurrent neural networks [46] , especially long short-term memory models [47], are supposed to better deal with temporal sequences [48], [49] , but it is out of the scope of this paper.”