• Corpus ID: 19313400

Supplementary material for the paper : Discriminative Correlation Filter with Channel and Spatial Reliability DCF-CSR

@inproceedings{Lukei2017SupplementaryMF,
  title={Supplementary material for the paper : Discriminative Correlation Filter with Channel and Spatial Reliability DCF-CSR},
  author={Alan Luke{\vz}i{\vc} and Tom{\'a}s Voj{\'i}r and Luka Cehovin Zajc and Jiri Matas and Matej Kristan},
  year={2017}
}
This is the supplementary material for the paper ”Discriminative Correlation Filter with Channel and Spatial Reliability” submitted to the CVPR 2017. Due to spatial constraints, parts not crucial for understanding the DCF-CSR tracker formulation, but helpful for gaining insights, were moved here. 1. Derivation of the augmented Lagrangian minimizer This section provides the complete derivation of the closed-form solutions for the relations (9,10) in the submitted paper [3]. The augmented… 

Figures from this paper

References

SHOWING 1-3 OF 3 REFERENCES
The Visual Object Tracking VOT2016 Challenge Results
TLDR
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
The Visual Object Tracking VOT2015 Challenge Results
TLDR
The Visual Object Tracking challenge 2015, VOT 2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.
The Visual Object Tracking VOT2015 Challenge Results
TLDR
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT 2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.