@inproceedings{Lebeda-ICCV2015,
  title     = {Exploring Causal Relationships in Visual Object Tracking},
  booktitle = {Proceedings of the International Conference on Computer Vision},
  author    = {Lebeda, Karel and Hadfield, Simon and Bowden, Richard},
  pages		= {},
  year      = {2015},
  month     = {December},
  day       = {13--16},
  venue     = {Santiago, Chile},
  pages     = {3065--3073},
  psurl     = {http://cvssp.org/Personal/KarelLebeda/papers/ICCV2015.pdf},
  annote    = {Causal relationships can often be found in visual object tracking
  				between the motions of the camera and that of the tracked
  				object. This object motion may be an effect of the camera
  				motion, e.g. an unsteady handheld camera. But it may also be the
  				cause, e.g. the cameraman framing the object. In this paper we
  				explore these relationships, and provide statistical tools to
  				detect and quantify them; these are based on transfer entropy
  				and stem from information theory. The relationships are then
  				exploited to make predictions about the object location. The
  				approach is shown to be an excellent measure for describing such
  				relationships. On the VOT2013 dataset the prediction accuracy is
  				increased by 62 % over the best non-causal predictor. We show
  				that the location predictions are robust to camera shake and
  				sudden motion, which is invaluable for any tracking algorithm
  				and demonstrate this by applying causal prediction to two
  				state-of-the-art trackers. Both of them benefit, Struck gaining
  				a 7 % accuracy and 22 % robustness increase on the VTB1.1
  				benchmark, becoming the new state-of-the-art.},
  keywords  = {Causality, Transfer entropy, Visual tracking, Camera motion, Trajectory},
  doi		= {10.1109/ICCV.2015.351},
  prestige  = {international},
  project   = {EPSRC EP/I011811/1, Rabin Ezra Scholarship},
  status    = {accepted}
}