@article{Lebeda-TIP2016,
  title     = {Texture-Independent Long-Term Tracking Using Virtual Corners},
  journal   = {IEEE Transactions on Image Processing},
  author    = {Lebeda, Karel and Hadfield, Simon and Matas, Ji{\v r}{\' \i} and Bowden, Richard},
  pages	    = {359--371},
  year      = {2016},
  month     = {Jan},
  volume    = {25},
  number    = {1},
  psurl     = {http://cvssp.org/Personal/KarelLebeda/papers/TIP2016.pdf},
  annote    = {Long term tracking of an object, given only a single instance in
				an initial frame, remains an open problem. We propose a visual
				tracking algorithm, robust to many of the difficulties which
				often occur in real-world scenes. Correspondences of edge-based
				features are used, to overcome the reliance on the texture of
				the tracked object and improve invariance to lighting.
				Furthermore we address long-term stability, enabling the tracker
				to recover from drift and to provide redetection following
				object disappearance or occlusion. The two-module principle is
				similar to the successful state-of-the-art long-term TLD
				tracker, however our approach offers better performance in
				benchmarks and extends to cases of low-textured objects. This
				becomes obvious in cases of plain objects with no texture at
				all, where the edge-based approach proves the most beneficial.
				We perform several different experiments to validate the
				proposed method. Firstly, results on short-term sequences show
				the performance of tracking challenging (low-textured and/or
				transparent) objects which represent failure cases for competing
				state-of-the-art approaches. Secondly, long sequences are
				tracked, including one of almost 30\,000 frames which to our
				knowledge is the longest tracking sequence reported to date.
				This tests the re-detection and drift resistance properties of
				the tracker. Finally, we report results of the proposed tracker
				on the VOT Challenge 2013 and 2014 datasets as well as on the
				VTB1.0 benchmark and we show relative performance of the tracker
				compared to its competitors. All the results are comparable to
				the state-of-the-art on sequences with textured objects and
				superior on non-textured objects. The new annotated sequences
				are made publicly available.},
  keywords  = {Machine vision, image motion analysis, visual tracking, long-term tracking, low texture, edge, line correspondence.},
  keywords2 = {edge detection;feature extraction;image sequences;image texture;
  				object tracking;VTB1.0 benchmark;drift resistance property;
  				edge-based feature detection;long-term stability;longest
  				tracking sequence;object disappearance;occlusion;
  				texture-independent object long-term tracking;two-module
  				principle;virtual corner;visual tracking algorithm;Apertures;
  				Image edge detection;Lighting;Robustness;Target tracking;
  				Visualization;Machine vision;edge;image motion analysis;line
  				correspondence;long-term tracking;low texture;visual tracking},
  doi       = {10.1109/TIP.2015.2497141},
  issn      = {1057-7149},
  project   = {EPSRC EP/I011811/1, GACR P103/12/G084},
  status    = {published}
}