Online Audience Measurement System Based on Machine Learning Techniques. Khryashchev, V., Priorov, A., & Ganin, A. In Distante, C, Battiato, S, & Cavallaro, A, editors, volume 8811, pages 111–122, 2014. doi abstract bibtex An application for video data analysis based on computer vision methods is presented. The proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of tracking. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 92 % accuracy of viewer's gender recognition is achieved. All the stages are united into a single system of audience analysis. The system allows to extract all the possible information about depicted people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters.
@inproceedings{khryashchev_online_2014,
title = {Online {Audience} {Measurement} {System} {Based} on {Machine} {Learning} {Techniques}},
volume = {8811},
isbn = {0302-9743},
doi = {10.1007/978-3-319-12811-5_8},
abstract = {An application for video data analysis based on computer vision methods is presented. The proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of tracking. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 92 \% accuracy of viewer's gender recognition is achieved. All the stages are united into a single system of audience analysis. The system allows to extract all the possible information about depicted people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters.},
author = {Khryashchev, Vladimir and Priorov, Andrey and Ganin, Alexander},
editor = {Distante, C and Battiato, S and Cavallaro, A},
year = {2014},
pages = {111--122},
}
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