Spatio-temporal SRU with global context-aware attention for 3D human action recognition

2020 ◽  
Vol 79 (17-18) ◽  
pp. 12349-12371
Author(s):  
Qingshan She ◽  
Gaoyuan Mu ◽  
Haitao Gan ◽  
Yingle Fan
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Context Aware ◽  
Global Context ◽  
Spatio Temporal

scholarly journals Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks

2018 ◽  
Vol 27 (4) ◽  
pp. 1586-1599 ◽  
Author(s):  
Jun Liu ◽  
Gang Wang ◽  
Ling-Yu Duan ◽  
Kamila Abdiyeva ◽  
Alex C. Kot
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Context Aware ◽  
Global Context

Spatio-Temporal VLAD Encoding for Human Action Recognition in Videos

2016 ◽  
pp. 365-378 ◽  
Author(s):  
Ionut C. Duta ◽  
Bogdan Ionescu ◽  
Kiyoharu Aizawa ◽  
Nicu Sebe
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Spatio Temporal

scholarly journals Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition

2020 ◽  
Vol 10 (12) ◽  
pp. 4412
Author(s):  
Ammar Mohsin Butt ◽  
Muhammad Haroon Yousaf ◽  
Fiza Murtaza ◽  
Saima Nazir ◽  
Serestina Viriri ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Human Action ◽  
Compact Representation ◽  
Agglomerative Clustering ◽  
Residual Vector ◽  
Benchmark Datasets ◽  
Codebook Generation ◽  
Spatio Temporal

Human action recognition has gathered significant attention in recent years due to its high demand in various application domains. In this work, we propose a novel codebook generation and hybrid encoding scheme for classification of action videos. The proposed scheme develops a discriminative codebook and a hybrid feature vector by encoding the features extracted from CNNs (convolutional neural networks). We explore different CNN architectures for extracting spatio-temporal features. We employ an agglomerative clustering approach for codebook generation, which intends to combine the advantages of global and class-specific codebooks. We propose a Residual Vector of Locally Aggregated Descriptors (R-VLAD) and fuse it with locality-based coding to form a hybrid feature vector. It provides a compact representation along with high order statistics. We evaluated our work on two publicly available standard benchmark datasets HMDB-51 and UCF-101. The proposed method achieves 72.6% and 96.2% on HMDB51 and UCF101, respectively. We conclude that the proposed scheme is able to boost recognition accuracy for human action recognition.

scholarly journals Study of Human Action Recognition Based on Improved Spatio-temporal Features

2014 ◽  
Vol 11 (5) ◽  
pp. 500-509 ◽  
Author(s):  
Xiao-Fei Ji ◽  
Qian-Qian Wu ◽  
Zhao-Jie Ju ◽  
Yang-Yang Wang
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Temporal Features ◽  
Spatio Temporal
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