Analysis of RSSI-based DFL for human detection in indoor environment using IRIS mote

2016 ◽  
Author(s):  
S. Shukri ◽  
L. M. Kamarudin ◽  
Goh Chew Cheik ◽  
R. Gunasagaran ◽  
A. Zakaria ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Human Detection

scholarly journals An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces

2019 ◽  
Author(s):  
Mohammad Mortazavi T. ◽  
Omid Mahdi Ebadati E.
Keyword(s):  
Human Skin ◽  
Skin Color ◽  
Wide Spectrum ◽  
Region Of Interest ◽  
Human Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Skin Detection ◽  
Color Spaces ◽  
Detection And Localization

Human Skin Detection is one of the most applicable methods in human detection, face detection and so many other detections. These processes can be used in a wide spectrum like industry, medicine, security, etc. The objective of this work is to provide an accurate and efficient method to detect human skin in images. This method can detect and classify skin pixels and reduce the size of images. With the use of RGB and YCbCr color spaces, proposed approach can localize a Region Of Interest (ROI) that contains skin pixels. This method consists of three steps. In the first stage, pre-processing an image like normalization, detecting skin range from the dataset, etc. is done. In the second stage, the proposed method detects candidate’s pixels that are in the range of skin color. In the third stage, with the use of a classifier, it decreases unwanted pixels and areas to decrease the accuracy of the region. The results show 97% sensitivity and 85% specificity for support vector machine classifier.

Robust human detection and localization in security applications

2016 ◽  
Vol 29 (23) ◽  
pp. e3977 ◽  
Author(s):  
Mozammel Chowdhury ◽  
Junbin Gao ◽  
Rafiqul Islam
Keyword(s):  
Human Detection ◽  
Security Applications ◽  
Detection And Localization

scholarly journals An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces

2019 ◽  
Author(s):  
Mohammad Mortazavi T. ◽  
Omid Mahdi Ebadati E.
Keyword(s):  
Human Skin ◽  
Skin Color ◽  
Wide Spectrum ◽  
Region Of Interest ◽  
Human Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Skin Detection ◽  
Color Spaces ◽  
Detection And Localization

Human Skin Detection is one of the most applicable methods in human detection, face detection and so many other detections. These processes can be used in a wide spectrum like industry, medicine, security, etc. The objective of this work is to provide an accurate and efficient method to detect human skin in images. This method can detect and classify skin pixels and reduce the size of images. With the use of RGB and YCbCr color spaces, proposed approach can localize a Region Of Interest (ROI) that contains skin pixels. This method consists of three steps. In the first stage, pre-processing an image like normalization, detecting skin range from the dataset, etc. is done. In the second stage, the proposed method detects candidate’s pixels that are in the range of skin color. In the third stage, with the use of a classifier, it decreases unwanted pixels and areas to decrease the accuracy of the region. The results show 97% sensitivity and 85% specificity for support vector machine classifier.

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