期刊
IEEE ACCESS
卷 8, 期 -, 页码 1840-1850出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2962284
关键词
Action recognition; residual learning; recurrent neural networks; long short-term memory (LSTM)
资金
- National Nature Science Foundation of China [61762023]
- Shaoguan Science and Technology Plan Project [2019sn064]
- Shaoguan University Research Project [SY2018KJ03, SZ2016KJ09]
- Shaoguan University Talent Introduction Research Project
One of challenging tasks in the field of artificial intelligence is the human action recognition. In this paper, we propose a novel long-term temporal feature learning architecture for recognizing human action in video, named Pseudo Recurrent Residual Neural Networks (P-RRNNs), which exploits the recurrent architecture and composes each in different connection among units. Two-stream CNNs model (GoogLeNet) is employed for extracting local temporal and spatial features respectively. The local spatial and temporal features are then integrated into global long-term temporal features by using our proposed two-stream P-RRNNs. Finally, the Softmax layer fuses the outputs of two-stream P-RRNNs for action recognition. The experimental results on two standard databases UCF101 and HMDB51 demonstrate the outstanding performance of proposed method based on architectures for human action recognition.
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