Geintra

Departamento de electronica Universidad de Alcala

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    People re-identification using depth and intensity information from an overhead camera

    TitlePeople re-identification using depth and intensity information from an overhead camera
    Publication TypeJournal Article
    Año de publicación2021
    AutoresLuna, CA, Losada-Gutiérrez, C, Fuentes-Jimenez, D, Mazo, M
    Idioma de publicaciónEnglish
    JournalExpert Systems with Applications
    Volumen182
    Páginas115287
    Fecha de publicación11/2021
    Palabras claveDepth information, Intensity images, Overhead depth camera, People re-identification, Real-time
    ISSN0957-4174
    URLhttps://www.sciencedirect.com/science/article/pii/S0957417421007181
    DOIhttps://doi.org/10.1016/j.eswa.2021.115287
    Abstract

    This work presents a new people re-identification method, using depth and intensity images, both of them captured with a single static camera, located in an overhead position. The proposed solution arises from the need that exists in many areas of application to carry out identification and re-identification processes to determine, for example, the time that people remain in a certain space, while fulfilling the requirement of preserving people’s privacy. This work is a novelty compared to other previous solutions, since the use of top-view images of depth and intensity allows obtaining information to perform the functions of identification and re-identification of people, maintaining their privacy and reducing occlusions. In the procedure of people identification and re-identification, only three frames of intensity and depth are used, so that the first one is obtained when the person enters the scene (frontal view), the second when it is in the central area of the scene (overhead view) and the third one when it leaves the scene (back view). In the implemented method only information from the head and shoulders of people with these three different perspectives is used. From these views three feature vectors are obtained in a simple way, two of them related to depth information and the other one related to intensity data. This increases the robustness of the method against lighting changes. The proposal has been evaluated in two different datasets and compared to other state-of-the-art proposal. The obtained results show a 96,7% success rate in re-identification, with sensors that use different operating principles, all of them obtaining depth and intensity information. Furthermore, the implemented method can work in real time on a PC, without using a GPU.