1 ## Tracking with Kernelized Correlation Filters
3 Code author : Tomas Vojir
7 This is a C++ reimplementation of algorithm presented in "High-Speed Tracking with Kernelized Correlation Filters" paper.
8 For more info and implementation in other languages visit the [autor's webpage!](http://home.isr.uc.pt/~henriques/circulant/).
10 It is extended by a scale estimation (use several *7* different scales steps) and
11 by a RGB (channels) and Color Names [2] features. Data for Color Names features were obtained from [SAMF tracker](https://github.com/ihpdep/samf).
13 It is free for research use. If you find it useful or use it in your research, please acknowledge my git repository
14 and cite the original paper [1].
16 The code depends on OpenCV 2.4+ library and is build via cmake toolchain.
21 for linux: open terminal in the directory with the code
23 $ mkdir build; cd build; cmake .. ; make
25 This code compiles into binary **kcf_vot**
28 - using VOT 2014 methodology (http://www.votchallenge.net/)
29 - INPUT : expecting two files, images.txt (list of sequence images with absolute path) and
30 region.txt with initial bounding box in the first frame in format "top_left_x, top_left_y, width, height" or
31 four corner points listed clockwise starting from bottom left corner.
32 - OUTPUT : output.txt containing the bounding boxes in the format "top_left_x, top_left_y, width, height"
35 - using VOT 2014+ trax protocol (http://www.votchallenge.net/)
36 - require [trax](https://github.com/votchallenge/trax) library to be compiled
37 with opencv support and installed. See trax instruction for compiling and
43 | | **VOT2016 - baseline EAO** | **VOT2016 - unsupervised EAO** | [**TV77**](http://cmp.felk.cvut.cz/~vojirtom/dataset/index.html) Avg. Recall |
44 |:---------------|:--------------:|:------------------:|:----------------:|
45 | kcf |0.1530 | 0.3859 | 51% |
46 | skcf |0.1661 | 0.4155 | 56% |
47 | skcf-cn |0.178 | 0.4136 | 58% |
48 | kcf-master |**18.67** | **0.4250** | **60%** |
53 [1] João F. Henriques, Rui Caseiro, Pedro Martins, Jorge Batista, “High-Speed Tracking with Kernelized Correlation Filters“,
54 IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
56 [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.
58 _____________________________________
59 Copyright (c) 2014, Tomáš Vojíř
61 Permission to use, copy, modify, and distribute this software for research
62 purposes is hereby granted, provided that the above copyright notice and
63 this permission notice appear in all copies.
65 THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
66 WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
67 MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
68 ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
69 WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
70 ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
71 OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
76 NOTE: The following files are part of Piotr's Toolbox, and were modified for usage with c++
78 src/piotr_fhog/gradientMex.cpp
79 src/piotr_fhog/sse.hpp
80 src/piotr_fhog/wrappers.hpp
82 You are encouraged to get the [full version of this library here.](http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html)
84 ______________________________________________________________________________
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