The popularity of wearable cameras is steadily increasing, both for entertainment and productivity purposes. Understanding the footage and inferring the body pose of the camera wearer can be of great importance in many fields, like medicine or robotics. It could help in monitoring rehabilitating patients at home from the hospital, in determining the acts of a law enforcement agent from the body camera or, in robotics, simplifying imitation learning based on video input or robot to worker coordination by estimating the posture of the operator. In this project, we aim to build a human motion dataset acquired indoors and outdoors using a GoPro and MVN Awinda, a movement tracking system based on inertial sensors that provide the 3D human pose. Next, the dataset has been used to train a Deep Neural Network to classify a sequence of frames in the task that it’s being performed (walking, running, .). Finally, a model for estimating the 3D pose of the camera wearer at each frame is proposed based on the same structure than the first network. The dataset is made of 300,000 frames, captured from seven different people, each one perform-ing 5-6 tasks in different scenarios, both indoors and outdoors. Each sequence of video frames has a synchronized sequence of 3D poses associated to it. Every 3D pose is composed of 23 segments. The vast majority of the code created and used in this project can be found in the GitHub repository for the project: https://github.com/BielColl/Human-Motion-Dataset-in-the-Wild-MAT-. Due to its size, the dataset built is not posted.


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    Titel :

    Human motion dataset in the wild (MAT)



    Erscheinungsdatum :

    2019-03-07


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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