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    Fast heuristic method to detect people in frontal depth images

    TítuloFast heuristic method to detect people in frontal depth images
    Tipo de publicaciónJournal Article
    Año de publicación2021
    AutoresLuna, CA, Losada-Gutiérrez, C, Fuentes-Jiménez, D, Mazo, M
    Idioma de publicaciónEnglish
    Revista académicaExpert Systems with Applications
    Fecha de publicación04/2021
    Palabras clave3D People detection, Depth camera, Feature extraction, Frontal Depth images, Head biometric classification

    This paper presents a new method for detecting people using only depth images captured by a camera in a frontal position. The approach is based on first detecting all the objects present in the scene and determining their average depth (distance to the camera). Next, for each object, a 3D Region of Interest (ROI) is processed around it in order to determine if the characteristics of the object correspond to the biometric characteristics of a human head. The results obtained using three public datasets captured by three depth sensors with different spatial resolutions and different operation principle (structured light, active stereo vision and Time of Flight) are presented. These results demonstrate that our method can run in realtime using a low-cost CPU platform with a high accuracy, being the processing times smaller than 1 ms per frame for a 512 × 424 image resolution with a precision of 99.26% and smaller than 4 ms per frame for a 1280 × 720 image resolution with a precision of 99.77%.

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