Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/112331
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Type: Journal article
Title: Two-stream 3-D convNet Fusion for action recognition in videos with arbitrary size and length
Author: Wang, X.
Gao, L.
Wang, P.
Sun, X.
Liu, X.
Citation: IEEE Transactions on Multimedia, 2018; 20(3):634-644
Publisher: IEEE
Issue Date: 2018
ISSN: 1520-9210
1941-0077
Statement of
Responsibility: 
Xuanhan Wang, Lianli Gao , Peng Wang, Xiaoshuai Sun and Xianglong Liu
Abstract: 3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3-D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112 × 112) input video; and 2) most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion, which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion. We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, and we adopt the LSTM/CNN-E model to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets).
Keywords: Action recognition; 3D convolution neural networks
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030086792
DOI: 10.1109/TMM.2017.2749159
Appears in Collections:Electrical and Electronic Engineering publications

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