## Tracking with Kernelized Correlation Filters
Code author : Tomas Vojir
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This is a C++ reimplementation of algorithm presented in "High-Speed Tracking with Kernelized Correlation Filters" paper.
For more info and implementation in other languages visit the [autor's webpage!](http://home.isr.uc.pt/~henriques/circulant/).
It is extended by a scale estimation (use several *7* different scales steps) and
by a RGB (channels) and Color Names [2] features. Data for Color Names features were obtained from [SAMF tracker](https://github.com/ihpdep/samf).
It is free for research use. If you find it useful or use it in your research, please acknowledge my git repository
and cite the original paper [1].
The code depends on OpenCV 2.4+ library and is build via cmake toolchain.
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Quick start guide
for linux: open terminal in the directory with the code
$ mkdir build; cd build; cmake .. ; make
This code compiles into binary **kcf_vot**
./kcf_vot
- using VOT 2014 methodology (http://www.votchallenge.net/)
- INPUT : expecting two files, images.txt (list of sequence images with absolute path) and
region.txt with initial bounding box in the first frame in format "top_left_x, top_left_y, width, height" or
four corner points listed clockwise starting from bottom left corner.
- OUTPUT : output.txt containing the bounding boxes in the format "top_left_x, top_left_y, width, height"
./kcf_trax
- using VOT 2014+ trax protocol (http://www.votchallenge.net/)
- require [trax](https://github.com/votchallenge/trax) library to be compiled
with opencv support and installed. See trax instruction for compiling and
installing.
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Performance
| | **VOT2016 - baseline EAO** | **VOT2016 - unsupervised EAO** | [**TV77**](http://cmp.felk.cvut.cz/~vojirtom/dataset/index.html) Avg. Recall |
|:---------------|:--------------:|:------------------:|:----------------:|
| kcf |0.1530 | 0.3859 | 51% |
| skcf |0.1661 | 0.4155 | 56% |
| skcf-cn |0.178 | 0.4136 | 58% |
| kcf-master |**0.1994** | **0.4376** | **63%** |
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References
[1] João F. Henriques, Rui Caseiro, Pedro Martins, Jorge Batista, “High-Speed Tracking with Kernelized Correlation Filters“,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
[2] J. van de Weijer, C. Schmid, J. J. Verbeek, and D. Larlus. "Learning color names for real-world applications." TIP, 18(7):1512–1524, 2009.
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Copyright (c) 2014, Tomáš Vojíř
Permission to use, copy, modify, and distribute this software for research
purposes is hereby granted, provided that the above copyright notice and
this permission notice appear in all copies.
THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
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Additional Library
NOTE: The following files are part of Piotr's Toolbox, and were modified for usage with c++
src/piotr_fhog/gradientMex.cpp
src/piotr_fhog/sse.hpp
src/piotr_fhog/wrappers.hpp
You are encouraged to get the [full version of this library here.](http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html)
______________________________________________________________________________
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