adelbibi / Target-Response-Adaptation-for-Correlation-Filter-Tracking

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"Target Response Adaptation for Correlation Filter Tracking" ECCV2016

Authors: Adel Bibi, Matthias Mueller, and Bernard Ghanem.

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   adel.bibi [AT]                 bibiadel93 [AT]
   matthias.mueller.2 [AT]
   Bernard.Ghanem [AT] 

   Adel Bibi:
   Bernard Ghanem:

Please cite:

  title={Target response adaptation for correlation filter tracking},
  author={Bibi, Adel and Mueller, Matthias and Ghanem, Bernard},
  booktitle={European Conference on Computer Vision},

This is a MATLAB implementation on the adaptive target for correlation filters.
The framework is generic and can be directly implemented in any correlation tracker that
solves the following objective. ||Ax - b||_2^2 + \lambda ||x||_2^2.

* The code is based on the tracker SAMF [1].

It is free for research use. If you find it useful, please acknowledge the paper
above with a reference.


The code is integratable with the OTB100 and OTB50 evaulation benchmarks.
To run the code over the complete benchmark:

1- Move the complete traker directory to the "Trackers" directory in the OTB evulation code.
Locate the function "configTrackers.m" in the OTB100 evaulation code. To install the OTB100 benchmark:

2- Add the following line to the list of trackers to be evualted over:
Note: The code that will be run through the evaulation by running the function "run_SAMF_AT.m".


[1] A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration. European Conference on Computer Vision Workshops 2014.
[2] Henriques, João F., et al. "High-speed tracking with kernelized correlation filters." Pattern Analysis and Machine Intelligence, IEEE Transactions on 37.3 (2015): 583-596.
[3] Henriques, Joao F., et al. "Exploiting the circulant structure of tracking-by-detection with kernels." Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012. 702-715.
[4] Wu, Yi, Jongwoo Lim, and Ming-Hsuan Yang. "Online object tracking: A benchmark." Proceedings of the IEEE conference on computer vision and pattern recognition. 2013.
A complete list of references can be found in the paper, which can be found here


Supplemental Material:


License:MIT License


Language:MATLAB 75.6%Language:C++ 24.4%