Paper Info
Title
An online people counting system for electronic advertising machines
Abstract
This paper presents a novel people counting system for an environment in which a stationary camera can count the number of people watching a TV-wall advertisement or an electronic billboard without counting the repetitions in video streams in real time. The people actually watching an advertisement are identified via frontal face detection techniques. To count the number of people precisely, a complementary set of features is extracted from the torso of a human subject, as that part of the body contains relatively richer information than the face. In addition, for conducting robust people recognition, an online classifier trained by Fisher's Linear Discriminant (FLD) strategy is developed. Our experiment results demonstrate the efficacy of the proposed system for the people counting task.
Year
DOI
Venue
2009
10.1109/ICME.2009.5202731
ICME
Keywords
Field
DocType
linear discriminant,novel people,tv-wall advertisement,frontal face detection technique,online people,electronic advertising machine,complementary set,human subject,experiment result,proposed system,electronic billboard,robust people recognition,face,object recognition,data mining,feature extraction,noise measurement,real time,image classification,face detection,shape,face recognition,kernel
Advertising,Computer science,Artificial intelligence,Face detection,Contextual image classification,Classifier (linguistics),Kernel (linear algebra),Computer vision,Facial recognition system,Feature extraction,Speech recognition,Linear discriminant analysis,Cognitive neuroscience of visual object recognition
Conference
ISSN
Citations 
PageRank 
1945-7871
5
0.49
References 
Authors
3
6
Name
Order
Citations
PageRank
Duan-Yu Chen129628.79
Chih-Wen Su217013.94
Yi-Chong Zeng39515.33
Shih-Wei Sun412720.28
Wei-Ru Lai525626.70
h y m liao62353198.72