Convolutional neural networks for detecting and mapping crowds in first person vision applications

J.S. Olier Jauregui, C.S. Regazzoni, L. Marcenaro, G.W.M. Rauterberg

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    1 Citation (Scopus)
    7 Downloads (Pure)


    There has been an increasing interest on the analysis of First Person Videos in the last few years due to the spread of low-cost wearable devices. Nevertheless, the understanding of the environment surrounding the wearer is a difficult task with many elements involved. In this work, a method for detecting and mapping the presence of people and crowds around the wearer is presented. Features extracted at the crowd level are used for building a robust representation that can handle the variations and occlusion of people’s visual characteristics inside a crowd. To this aim, convolutional neural networks have been exploited. Results demonstrate that this approach achieves a high accuracy on the recognition of crowds, as well as the possibility of a general interpretation of the context trough the classification of characteristics of the segmented background.
    Original languageEnglish
    Title of host publicationAdvances in Computational Intelligence : 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part I
    EditorsI. Rojas, G. Joya
    Place of PublicationBerlin
    ISBN (Print)9783319192574
    Publication statusPublished - 2015

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743


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