People identification for domestic non-overlapping RGB-D camera networks

B. Takac, G.W.M. Rauterberg, A. Català, W. Chen

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

    6 Citations (Scopus)


    The ability to identify the specific person in a home camera network is very relevant for healthcare applications where humans need to be observed daily in their living environment. The appearance based people identification in a domestic environment has many similarities with the problem of re-identification in public surveillance systems, but there are also some additional beneficial and constraining factors (e.g., less people, non-pedestrian behaviour, unusual camera viewpoints). In this paper, we are considering the problem of people identification in a small home RGB-D camera network, for which we have developed a method based on appearance learning and classification using a combination of SVM and the Naive Bayes classifier. The method is evaluated using the prototype of a real-time multiple camera system on a 16 people dataset.
    Original languageEnglish
    Title of host publicationProceedings of 11th International Multi-Conference on Systems, Signals & Devices - SSD 2014, 11-14 February 2014, Barcelona, Spain
    Place of PublicationPiscataway
    PublisherInstitute of Electrical and Electronics Engineers
    ISBN (Print)978-1-4799-3866-7/14/$31.00
    Publication statusPublished - 2015


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