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Video Analysis System Using Deep Learning Algorithms

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Ambient Intelligence – Software and Applications (ISAmI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1239))

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Abstract

Detection of video duplicates is an active field of research, motivated by the protection of intellectual property, the fight against piracy or the tracing of the origin of reused video segments.

In this work, a method for the detection of duplicate videos is proposed and implemented, making use of deep learning methods and techniques typical of the field of information recovery. This method has been evaluated with a data set usually used in the field, with which high average accuracies, above 85%, have been obtained. The effect of the different layers of the convolutional neural network used by the algorithm, the aggregation mechanisms that can be used on them, and the influence of the recovery model have been studied, finding a set of parameters that optimize the overall accuracy of the system.

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Notes

  1. 1.

    http://www.scopus.com.

  2. 2.

    https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet.

  3. 3.

    Although in our case this is not possible due to the use of the code book.

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Acknowledgments

This research has been supported by the project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGEMobility): Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security”, Ref.: RTI2018-095390-B-C32, (MCIU/AEI/FEDER, UE).

Author information

Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+i Calle Espejo 2, Salamanca, Spain

    Guillermo Hernández, Sara Rodríguez, Angélica González, Juan Manuel Corchado & Javier Prieto

Authors
  1. Guillermo Hernández
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  2. Sara Rodríguez
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  3. Angélica González
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  4. Juan Manuel Corchado
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  5. Javier Prieto
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Corresponding author

Correspondence to Sara Rodríguez .

Editor information

Editors and Affiliations

  1. Departamento de Informática, University of Minho, ALGORITMI Center, Braga, Portugal

    Paulo Novais

  2. DIBRIS, University of Genoa, Genoa, Italy

    Gianni Vercelli

  3. Data Management Group, Technical University of Catalonia, Barcelona, Barcelona, Spain

    Josep L. Larriba-Pey

  4. Department Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, Granada, Spain

    Francisco Herrera

  5. BISITE Research Group, University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

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Hernández, G., Rodríguez, S., González, A., Corchado, J.M., Prieto, J. (2021). Video Analysis System Using Deep Learning Algorithms. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds) Ambient Intelligence – Software and Applications . ISAmI 2020. Advances in Intelligent Systems and Computing, vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-58356-9_19

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