Open AccessPosted Content
Convolutional Two-Stream Network Fusion for Video Action Recognition
TLDR
In this paper, a spatial and temporal network can be fused at the last convolution layer without loss of performance, but with a substantial saving in parameters, and furthermore, pooling of abstract convolutional features over spatiotemporal neighbourhoods further boosts performance.Abstract:
Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information. We study a number of ways of fusing ConvNet towers both spatially and temporally in order to best take advantage of this spatio-temporal information. We make the following findings: (i) that rather than fusing at the softmax layer, a spatial and temporal network can be fused at a convolution layer without loss of performance, but with a substantial saving in parameters; (ii) that it is better to fuse such networks spatially at the last convolutional layer than earlier, and that additionally fusing at the class prediction layer can boost accuracy; finally (iii) that pooling of abstract convolutional features over spatiotemporal neighbourhoods further boosts performance. Based on these studies we propose a new ConvNet architecture for spatiotemporal fusion of video snippets, and evaluate its performance on standard benchmarks where this architecture achieves state-of-the-art results.read more
Citations
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Journal ArticleDOI
Exploring hybrid spatio-temporal convolutional networks for human action recognition
TL;DR: This paper proposes a novel hybrid spatio-temporal convolutional network for action recognition that integrates three different type of streams into the network and can take full advantage of the spatio/temporal information of the videos.
Journal ArticleDOI
A CRNN module for hand pose estimation
TL;DR: A convolutional recurrent neural network (CRNN) module is proposed, which combines the characteristics of Convolutional Neural Network (CNN) and Recurrent Neural network (RNN) and can significantly improve the accuracy of the network.
Journal ArticleDOI
Feature refinement for image-based driver action recognition via multi-scale attention convolutional neural network
Yaocong Hu,Mingqi Lu,Xiaobo Lu +2 more
TL;DR: The quantitative experiment result shows that the proposed multi-scale attention convolutional neural network (MSA-CNN) obtains the state of the art performance in image-based driver action recognition.
Journal ArticleDOI
Patient Monitoring by Abnormal Human Activity Recognition Based on CNN Architecture
TL;DR: The proposed approach differentiated abnormal actions with improved F1-Score of 89.2% which is higher than state-of-the-art techniques which indicates that the proposed framework can be beneficial to hospitals and elder care homes for patient monitoring.
Posted Content
Semi-Coupled Two-Stream Fusion ConvNets for Action Recognition at Extremely Low Resolutions
TL;DR: A semi-coupled, filter-sharing network that leverages highresolution (HR) videos during training in order to assist an eLR ConvNet and outperforms state-of-the-art methods at extremely low resolutions on IXMAS and HMDB datasets.
References
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Proceedings Article
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Proceedings ArticleDOI
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