Citation
Nikolay A. Atanasov, Bharath Sankaran, Jerome Le Ny, George Pappas, Kostas Daniilidis. "Nonmyopic View Planning for Active Object Detection". IEEE Transactions on Robotics (TRO), September 2013; 12 pages (two-column); 7 figures; 2 tables; Manuscript submitted to the IEEE Transactions on Robotics (TRO).

Abstract
One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of views, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and real-world experiments with the PR2 robot. The results suggest that our approach outperforms the widely-used greedy view point selection and provides a significant improvement over static object detection.

Electronic downloads

• http://arxiv.org/pdf/1309.5401.pdf

• HTML

  Nikolay A. Atanasov, Bharath Sankaran, Jerome Le Ny, George
  Pappas, Kostas Daniilidis. <a
  href="http://www.terraswarm.org/pubs/125.html"
  >Nonmyopic View Planning for Active Object
  Detection</a>, <i>IEEE Transactions on Robotics
  (TRO)</i>, September 2013; 12 pages (two-column); 7
  figures; 2 tables; Manuscript submitted to the IEEE
  Transactions on Robotics (TRO).

• Plain text

  Nikolay A. Atanasov, Bharath Sankaran, Jerome Le Ny, George
  Pappas, Kostas Daniilidis. "Nonmyopic View Planning for
  Active Object Detection". <i>IEEE Transactions on
  Robotics (TRO)</i>, September 2013; 12 pages
  (two-column); 7 figures; 2 tables; Manuscript submitted to
  the IEEE Transactions on Robotics (TRO).

• BibTeX

  @article{AtanasovSankaranLeNyPappasDaniilidis13_NonmyopicViewPlanningForActiveObjectDetection,
      author = {Nikolay A. Atanasov and Bharath Sankaran and
                Jerome Le Ny and George Pappas and Kostas
                Daniilidis},
      title = {Nonmyopic View Planning for Active Object Detection},
      journal = {IEEE Transactions on Robotics (TRO)},
      month = {September},
      year = {2013},
      note = {12 pages (two-column); 7 figures; 2 tables;
                Manuscript submitted to the IEEE Transactions on
                Robotics (TRO)},
      abstract = {One of the central problems in computer vision is
                the detection of semantically important objects
                and the estimation of their pose. Most of the work
                in object detection has been based on single image
                processing and its performance is limited by
                occlusions and ambiguity in appearance and
                geometry. This paper proposes an active approach
                to object detection by controlling the point of
                view of a mobile depth camera. When an initial
                static detection phase identifies an object of
                interest, several hypotheses are made about its
                class and orientation. The sensor then plans a
                sequence of views, which balances the amount of
                energy used to move with the chance of identifying
                the correct hypothesis. We formulate an active
                hypothesis testing problem, which includes sensor
                mobility, and solve it using a point-based
                approximate POMDP algorithm. The validity of our
                approach is verified through simulation and
                real-world experiments with the PR2 robot. The
                results suggest that our approach outperforms the
                widely-used greedy view point selection and
                provides a significant improvement over static
                object detection.},
      URL = {http://terraswarm.org/pubs/125.html}
  }
  

Posted by Nikolay A. Atanasov on 29 Sep 2013.