Bonn Activity Maps: Dataset Description

Julian Tanke, Oh-Hun Kwon, Patrick Stotko, Radu Alexandru Rosu, Michael Weinmann, Hassan Errami, Sven Behnke, Maren Bennewitz, Reinhard Klein, Andreas Weber, Angela Yao und Juergen Gall
arXiv:1912.06354, Dez. 2019
 

Abstract

The key prerequisite for accessing the huge potential of current machine learning techniques is the availability of large databases that capture the complex relations of interest. Previous datasets are focused on either 3D scene representations with semantic information, tracking of multiple persons and recognition of their actions, or activity recognition of a single person in captured 3D environments. We present Bonn Activity Maps, a large-scale dataset for human tracking, activity recognition and anticipation of multiple persons. Our dataset comprises four different scenes that have been recorded by time-synchronized cameras each only capturing the scene partially, the reconstructed 3D models with semantic annotations, motion trajectories for individual people including 3D human poses as well as human activity annotations. We utilize the annotations to generate activity likelihoods on the 3D models called activity maps.

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Bibtex

@UNPUBLISHED{bonn_activity_maps_arxiv,
    author = {Tanke, Julian and Kwon, Oh-Hun and Stotko, Patrick and Rosu, Radu Alexandru and Weinmann, Michael
              and Errami, Hassan and Behnke, Sven and Bennewitz, Maren and Klein, Reinhard and Weber, Andreas and
              Yao, Angela and Gall, Juergen},
     title = {Bonn Activity Maps: Dataset Description},
      year = {2019},
     month = dec,
      note = {arXiv:1912.06354},
  abstract = {The key prerequisite for accessing the huge potential of current machine learning techniques is the
              availability of large databases that capture the complex relations of interest. Previous datasets
              are focused on either 3D scene representations with semantic information, tracking of multiple
              persons and recognition of their actions, or activity recognition of a single person in captured 3D
              environments. We present Bonn Activity Maps, a large-scale dataset for human tracking, activity
              recognition and anticipation of multiple persons. Our dataset comprises four different scenes that
              have been recorded by time-synchronized cameras each only capturing the scene partially, the
              reconstructed 3D models with semantic annotations, motion trajectories for individual people
              including 3D human poses as well as human activity annotations. We utilize the annotations to
              generate activity likelihoods on the 3D models called activity maps.}
}