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    GEINTRA Overhead ToF People Detection (GOTPD1 & GOTPD2) Databases


    The GOTPD1 & GOTPD2 (Geintra Overhead ToF People Detection datasets) are multimodal databases (depth, and infrared data in the case of GOTPD1; and depth, infrared and RGB data in the case of GOTPD2 (RGB only for frontal camera)) of recordings from a Kinect 2 camera located in overhead/frontal position, monitoring people movements under/in front of it, and it was designed to fulfill the following objectives: 

    • Allow evaluation and fine tuning of the ToF data adquisition system in the GEINTRA research group.
    • Allow the evaluation of people detection, headgear accesories identification and human activity detection algorithms based on data generated by ToF cameras (Including RGB, depth and infrared) placed in overhead and frontal positions.
    • Provide quality data to the research community in people detection and identification tasks.

    The people detection task (and the data provided) can also be extended to practical applications
    such as video-surveillance, access control, people flow analysis, behaviour analysis or event capacity

    Sample videos

    We provide here some video examples generated from recordings from the database (depth only):

    Additional information

    You can access full details on the dataset in the corresponding document, and get a data sample (see the documentation for references on file formats). The documents refer mainly to the GOTPD1 dataset.

    If you want to get a copy of the datasets, please contact or download it from kaggle.  Suplemental data (23 additional sequences) that we have used to extend the GOTPD1 data to face DNN based approaches can be found at this additional link (we stored it there as kaggle does not support datasets over 20Gbytes).


    Creative Commons License
    GEINTRA Overhead ToF People Detection (GOTPD1&GOTPD2) Databases by Javier Macias-Guarasa, Cristina Losada-Gutierrez, David Fuentes-Jimenez, Raquel Garcia-Jimenez, Carlos A. Luna, Alvaro Fernandez-Rincon, and Manuel Mazo is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

    If you make use of this databases and/or its related documentation, you are kindly requested to cite the paper:



    usersrandom.mp418.89 MB
    usersgrouped.mp45.46 MB
    userfists.mp44.17 MB
    userchair.mp44.6 MB

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