scholarly journals Utilising mobile phone RSSI metric for human activity detection

2009 ◽  
Author(s):  
J. Doyle ◽  
R. Farrell ◽  
S. McLoone ◽  
T. McCarthy ◽  
M. Tahir ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Human Activity ◽  
Activity Detection ◽  
Human Activity Detection

Human Activity Detection Using Deep Learning and Bracelet with Bluetooth Transmitter

2021 ◽  
Author(s):  
Stevan Cakic ◽  
Stevan Sandi ◽  
Daliborka Nedic ◽  
Srdan Krco ◽  
Tomo Popovic
Keyword(s):  
Deep Learning ◽  
Human Activity ◽  
Activity Detection ◽  
Human Activity Detection

ML-Based Device-Agnostic Human Activity Detection with WiFi Sniffer Traffic

2022 ◽  
Author(s):  
Harshit Grover ◽  
Dheryta Jaisinghani ◽  
Nishtha Phutela ◽  
Shivani Mittal
Keyword(s):  
Human Activity ◽  
Activity Detection ◽  
Human Activity Detection

scholarly journals Abnormal Human Activity Detection using Unsupervised Machine Learning Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 3949-3953
Keyword(s):  
Machine Learning ◽  
Human Activity ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Abnormal Behavior ◽  
Activity Detection ◽  
Research Attention ◽  
Learning Techniques ◽  
Abnormal Behavior Detection ◽  
Human Activity Detection

Nowadays there is a significant study effort due to the popularity of CCTV to enhance analysis methods for surveillance videos and video-based images in conjunction with machine learning techniques for the purpose of independent assessment of such information sources. Although recognition of human intervention in computer vision is extremely attained subject, abnormal behavior detection is lately attracting more research attention. In this paper, we are interested in the studying the two main steps that compose abnormal human activity detection system which are the behavior representation and modelling. And we use different techniques, related to feature extraction and description for behavior representation as well as unsupervised classification methods for behavior modelling. In addition, available datasets and metrics for performance evaluation will be presented. Finally, this paper will be aimed to detect abnormal behaved object in crowd, such as fast motion in a crowd of walking people

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