Paper Info
Title
Safety helmet wearing detection based on image processing and machine learning
Abstract
Safety helmet wearing detection is very essential in power substation. This paper proposed a innovative and practical safety helmet wearing detection method based on image processing and machine learning. At first, the ViBe background modelling algorithm is exploited to detect motion object under a view of fix surveillant camera in power substation. After obtaining the motion region of interest, the Histogram of Oriented Gradient (HOG) feature is extracted to describe inner human. And then, based on the result of HOG feature extraction, the Support Vector Machine (SVM) is trained to classify pedestrians. Finally, the safety helmet detection will be implemented by color feature recognition. Compelling experimental results demonstrated the correctness and effectiveness of our proposed method.
Year
DOI
Venue
2017
10.1109/ICACI.2017.7974509
2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)
Keywords
Field
DocType
vibe,histogram of oriented gradient,support vector machine,color feature recognition
Computer vision,Histogram,Feature (computer vision),Computer science,Correctness,Support vector machine,Feature recognition,Image processing,Feature extraction,Artificial intelligence,Region of interest,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5090-4727-7
0
0.34
References 
Authors
5
7
Name
Order
Citations
PageRank
J.X. Li1403113.63
Huanming Liu200.34
Tianzheng Wang301.35
Min Jiang44015.95
Shuai Wang525248.81
Kang Li660779.66
Xiaoguang Zhao75418.68