@inproceedings{Lebeda-ACCV2014,
  title     = {2D Or Not 2D: Bridging the Gap Between Tracking and Structure from Motion},
  author    = {Lebeda, Karel and Hadfield, Simon and Bowden, Richard},
  authorship= {34-33-33},
  year      = {2015},
  year_of_conference= {2014},
  month     = {November},
  day       = {1--5},
  pages     = {642--658},
  booktitle = {Computer Vision -- ACCV 2014},
  publisher = {Springer International Publishing},
  series    = {LNCS},
  volume    = {9006},
  address   = {Heidelberg, Germany},
  editor    = {Cremers, Daniel and Reid, Ian and Saito, Hideo and Yang, Ming-Hsuan},
  isbn      = {978-3-319-16816-6},
  issn      = {0302-9743},
  venue     = {NUS, Singapore},
  psurl     = {http://cvssp.org/Personal/KarelLebeda/papers/ACCV2014.pdf},
  url       = {http://dx.doi.org/10.1007/978-3-319-16817-3_42},
  annote    = {In this paper, we address the problem of tracking an unknown object
in 3D space. Online 2D tracking often fails for strong out-of-plane rotation which
results in considerable changes in appearance beyond those that can be represented
by online update strategies. However, by modelling and learning the 3D structure
of the object explicitly, such effects are mitigated. To address this, a novel
approach is presented, combining techniques from the fields of visual tracking,
structure from motion (SfM) and simultaneous localisation and mapping (SLAM). This
algorithm is referred to as TMAGIC (Tracking, Modelling And Gaussian-process
Inference Combined). At every frame, point and line features are tracked in the
image plane and are used, together with their 3D correspondences, to estimate the
camera pose. These features are also used to model the 3D shape of the object as a
Gaussian process. Tracking determines the trajectories of the object in both the
image plane and 3D space, but the approach also provides the 3D object shape. The
approach is validated on several video-sequences used in the tracking literature,
comparing favourably to state-of-the-art trackers for simple scenes (error reduced
by 22 %) with clear advantages in the case of strong out-of-plane rotation, where
2D approaches fail (error reduction of 58 %).},
  keywords  = {Visual tracking, Structure from Motion, SLAM, 3D Tracking, Gaussian Process},
  language  = {English},
  prestige  = {international},
  project   = {EPSRC EP/I011811/1, BMVA student Travel Grant},
  status    = {published},
  book_pages= {731},
  doi       = {10.1007/978-3-319-16817-3_42},
}