Data Driven Sensing for Action Recognition Using Deep Convolutional Neural Networks

2019 ◽  
pp. 250-259
Author(s):  
Ronak Gupta ◽  
Prashant Anand ◽  
Vinay Kaushik ◽  
Santanu Chaudhury ◽  
Brejesh Lall
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Data Driven ◽  
Deep Convolutional Neural Networks

Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

2017 ◽  
Vol 157 ◽  
pp. 322-330 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan ◽  
Siddhartha K. Khaitan ◽  
Alok Choudhary ◽  
Ankit Agrawal
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Data Driven ◽  
Deep Convolutional Neural Networks ◽  
Pavement Distress ◽  
Pavement Distress Detection

scholarly journals Spatio–Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1932 ◽  
Author(s):  
Huy Hieu Pham ◽  
Houssam Salmane ◽  
Louahdi Khoudour ◽  
Alain Crouzil ◽  
Pablo Zegers ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Large Scale ◽  
Image Representation ◽  
Human Action ◽  
Computational Time ◽  
Deep Convolutional Neural Networks ◽  
Classification Tasks ◽  
Spatio Temporal

Designing motion representations for 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio–temporal patterns of skeletal movements and how to learn their discriminative features for classification tasks. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

Geometry and boundary condition adaptive data-driven model of fluid flow based on deep convolutional neural networks

2021 ◽  
Vol 33 (12) ◽  
pp. 123602
Author(s):  
Jiang-Zhou Peng ◽  
Nadine Aubry ◽  
Shiquan Zhu ◽  
Zhihua Chen ◽  
Wei-Tao Wu
Keyword(s):  
Neural Networks ◽  
Boundary Condition ◽  
Fluid Flow ◽  
Convolutional Neural Networks ◽  
Data Driven ◽  
Deep Convolutional Neural Networks
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