Action recognition using optimized deep autoencoder and CNN for surveillance data streams of non-stationary environments-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Action recognition using optimized deep autoencoder and CNN for surveillance data streams of non-stationary environments

Published:2019-07 Issue: Volume:96 Page:386-397
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Ullah Amin^ORCID,Muhammad Khan,Haq Ijaz Ul,Baik Sung Wook

Funder

Korea Government

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference60 articles.

1. Recognizing complex activities by a probabilistic interval-based model;Liu,2016

2. An adaptive semisupervised feature analysis for video semantic recognition;Luo;IEEE Trans. Cybern.,2018

3. From action to activity: sensor-based activity recognition;Liu;Neurocomputing,2016

4. Action recognition in surveillance video using convnets and motion history image;Luo,2016

5. Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking;Liu,2012

Cited by 130 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. From CNNs to Transformers in Multimodal Human Action Recognition: A Survey;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-07-09

2. Efficient Online Big Data Stream Clustering Using Dual Interactive Wasserstein Generative Adversarial Network;International Journal on Artificial Intelligence Tools;2024-06-12

3. An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection;Applied Sciences;2024-05-30

4. Domain-Adaptive and Context-Aware Fall Detection Based on Coarse-Fine Network Learning;International Journal of Innovative Science and Research Technology (IJISRT);2024-05-23

5. Towards Real-world Violence Recognition via Efficient Deep Features and Sequential Patterns Analysis;Mobile Networks and Applications;2024-05-20

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3