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Online Audience Measurement System Based on Machine Learning Techniques

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Video Analytics for Audience Measurement (VAAM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8811))

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.

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Author information

Authors and Affiliations

  1. P.G. Demidov Yaroslavl State University, Yaroslavl, Russia

    Vladimir Khryashchev, Andrey Priorov & Alexander Ganin

Authors
  1. Vladimir Khryashchev
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  2. Andrey Priorov
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  3. Alexander Ganin
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Corresponding author

Correspondence to Vladimir Khryashchev .

Editor information

Editors and Affiliations

  1. National Research Council of Italy, Arnesano, Lecce, Italy

    Cosimo Distante

  2. Dipartimento di Matematica e Informatica, University of Catania IT, Catania, Catania, Italy

    Sebastiano Battiato

  3. Queen Mary University of London, London, United Kingdom

    Andrea Cavallaro

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Khryashchev, V., Priorov, A., Ganin, A. (2014). Online Audience Measurement System Based on Machine Learning Techniques. In: Distante, C., Battiato, S., Cavallaro, A. (eds) Video Analytics for Audience Measurement. VAAM 2014. Lecture Notes in Computer Science(), vol 8811. Springer, Cham. https://doi.org/10.1007/978-3-319-12811-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-12811-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12810-8

  • Online ISBN: 978-3-319-12811-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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