NDLI: Structured RNN for human interaction

Content Provider	IET Digital Library
Author	Truong, Anh Minh Yoshitaka, Atsuo
Abstract	Understanding human activities has been an important research area in computer vision. Generally, the authors can model the human interactions as a temporal sequence with the transition in relationships of humans and objects. Besides, many studies have proved the effectiveness of long short-term memory (LSTM) on long-term temporal dependency problems. Here, the authors proposed a novel structured recurrent neural network (S-RNN) to model spatio-temporal relationships between human subjects and objects in daily human interactions. The authors represent the evolution of different components and the relationships between them over time by several subnets. Then, the hidden representations of those relations are fused and fed into the later layers to obtain the final hidden representation. The final prediction is carried out by the single-layer perceptron. The experimental results of different tasks on the CAD-120, SBU-Kinect-Interaction, multi-modal and multi-view and interactive, and NTU RGB+D data sets showed advantages of the proposed method compared with the state-of-art methods.
Starting Page	817
Ending Page	825
Page Count	9
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 6, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0487
Journal	IET Computer Vision
Publisher Date	2018-04-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CAD-120 Computer Vision Computer Vision And Image Processing Technique Human Activities Human Interaction Long Short-term Memory Long-term Temporal Dependency Problem Neural Computing Technique Optical, Image And Video Signal Processing Perceptrons Recurrent Neural Nets S-RNN SBU-Kinect-Interaction Single-layer Perceptron Spatio-temporal Relationships Structured Recurrent Neural Network Temporal Sequence
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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