Robust scale-adaptive mean-shift for tracking
所属分类：Windows编程
开发工具：C/C++
文件大小：347KB
下载次数：9
上传日期：2018-09-08 16:18:34
上 传 者：
snail222
说明： 该算法是VOT2015的第20名官方推荐的实时算法，VOT2016的32名(中等水平)，平均帧率125FPS，在经典mean-shift框架下加入了尺度估计，经典颜色直方图特征，
加入了两个先验(尺度不剧变+可能偏最大)作为正则项，和反向尺度一致性检查。
(The algorithm is the real-time algorithm recommended by the 20th official VOT 2010, 32 (medium level) VOT 2010 6, average frame rate of 125 FPS, scale estimation and classical color histogram features are added in the classical mean-shift framework.
Two priors (scale unchanged + possible maximum) are added as the regular term and the reverse scale consistency check is added.)
文件列表:
CMakeLists.txt (1119, 2017-05-16)
main_demo.cpp (2993, 2017-07-14)
main_trax.cpp (1496, 2017-07-09)
main_vot.cpp (1666, 2017-05-16)
src\CMakeLists.txt (250, 2017-05-16)
src\colotracker.cpp (16454, 2017-05-16)
src\colotracker.h (2180, 2017-05-16)
src\histogram.cpp (3103, 2017-05-16)
src\histogram.h (942, 2017-05-16)
src\region.cpp (4712, 2017-05-16)
src\region.h (1487, 2017-05-16)
vot.hpp (5024, 2017-05-16)
Robust scale-adaptive mean-shift for tracking\ASMS追踪算法.txt (518, 2017-07-07)
Robust scale-adaptive mean-shift for tracking\Robust scale-adaptive mean-shift for tracking.pdf (370641, 2017-07-07)
src (0, 2017-07-07)
Robust scale-adaptive mean-shift for tracking (0, 2017-07-07)
## Robust scale-adaptive mean-shift for tracking
Authors : Tomas Vojir, Jana Noskova, Jiri Matas
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This C++ code implements a tracking pipeline of Scale Adaptive Mean-Shift method.
It is free for research use. If you find it useful or use it in your research, please cite the [1] paper.
The code depends on OpenCV 2.4+ library and is build via cmake toolchain.
_________________
Quick start guide
for linux: open terminal in the directory with the code
$ mkdir build; cd build; cmake .. ; make
This code compiles into two binaries **demo** and **asms_vot**
./asms_demo
- run live demo with visual output
- control : object is selected by mouse, click and drag mouse to select rectandle
- ESC = quit
- i = init new object
./asms_vot
- using VOT 2015 methodology (http://www.votchallenge.net/), is backward compatible with older ones
- 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"
- OUTPUT : output.txt containing the bounding boxes in the same format as region.txt
./asms_trax
- using VOT2014+ trax protocol
- 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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References
[1] Tomas Vojir, Jana Noskova and Jiri Matas, “Robust scale-adaptive mean-shift for tracking“.
Pattern Recognition Letters 2014.
_____________________________________
Copyright (c) 2014, Tomas Vojir
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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