We present a multitask network that supports various deep neural network based pedestrian detection functions. Besides 2D and 3D human pose, it also supports body and head orientation estimation based on full body bounding box input. This eliminates the need for explicit face recognition. We show that the performance of 3D human pose estimation and orientation estimation is comparable to the state-of-the-art. Since very few data sets exist for 3D human pose and in particular body and head orientation estimation based on full body data, we further show the benefit of particular simulation data to train the network. The network architecture is relatively simple, yet powerful, and easily adaptable for further research and applications.


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    Title :

    PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation


    Contributors:


    Publication date :

    2022-06-05


    Size :

    2196860 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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