Paper Info
Title
Co-Saliency-Enhanced Deep Recurrent Convolutional Networks for Human Fall Detection in E-Healthcare.
Abstract
This paper addresses the issue of fall detection from videos for e-healthcare and assisted-living. Instead of using conventional hand-crafted features from videos, we propose a fall detection scheme based on co-saliency-enhanced recurrent convolutional network (RCN) architecture for fall detection from videos. In the proposed scheme, a deep learning method RCN is realized by a set of Convolutional Neural Networks (CNNs) in segment-levels followed by a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), to handle the time-dependent video frames. The co-saliency-based method enhances salient human activity regions hence further improves the deep learning performance. The main contributions of the paper include: (a) propose a recurrent convolutional network (RCN) architecture that is dedicated to the tasks of human fall detection in videos; (b) integrate a co-saliency enhancement to the deep learning scheme for further improving the deep learning performance; (c) extensive empirical tests for performance analysis and evaluation under different network settings and data partitioning. Experiments using the proposed scheme were conducted on an open dataset containing multicamera videos from different view angles, results have shown very good performance (test accuracy 98.96%). Comparisons with two existing methods have provided further support to the proposed scheme.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512586
EMBC
Field
DocType
Volume
Computer vision,Pattern recognition,Salience (neuroscience),Convolutional neural network,Computer science,Network architecture,Recurrent neural network,Feature extraction,Image segmentation,Artificial intelligence,Deep learning,Salient
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Chenjie Ge100.34
Irene Yu-Hua Gu261335.06
Jie Yang31392157.55