# Relative_tracker

**Repository Path**: chenxingyusean/Relative_tracker

## Basic Information

- **Project Name**: Relative_tracker
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-03-28
- **Last Updated**: 2020-12-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# C++ KCF Tracker
This package includes a C++ class with several tracking methods based on the Kernelized Correlation Filter (KCF) [1, 2].   
It also includes an executable to interface with the VOT benchmark.

[1] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,   
"High-Speed Tracking with Kernelized Correlation Filters", TPAMI 2015.

[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,   
"Exploiting the Circulant Structure of Tracking-by-detection with Kernels", ECCV 2012.


Authors: Joao Faro, Christian Bailer, Joao F. Henriques   
Contacts: joaopfaro@gmail.com, Christian.Bailer@dfki.de, henriques@isr.uc.pt   
Institute of Systems and Robotics - University of Coimbra / Department of Augmented Vision DFKI   

### Algorithms (in this folder) ###

"KCFC++", command: ./KCF   
Description: KCF on HOG features, ported to C++ OpenCV. The original Matlab tracker placed 3rd in VOT 2014.

"KCFLabC++", command: ./KCF lab   
Description: KCF on HOG and Lab features, ported to C++ OpenCV. The Lab features are computed by quantizing CIE-Lab colors into 15 centroids, obtained from natural images by k-means.   

The CSK tracker [2] is also implemented as a bonus, simply by using raw grayscale as features (the filter becomes single-channel).   

### Compilation instructions ###
There are no external dependencies other than OpenCV 3.0.0. Tested on a freshly installed Ubuntu 14.04.   

1) cmake CMakeLists.txt   
2) make   

### Running instructions ###

The runtracker.cpp is prepared to be used with the VOT toolkit. The executable "KCF" should be called as:   

./KCF [OPTION_1] [OPTION_2] [...]

Options available:   

gray - Use raw gray level features as in [1].   
hog - Use HOG features as in [2].   
lab - Use Lab colorspace features. This option will also enable HOG features by default.   
singlescale - Performs single-scale detection, using a variable-size window.   
fixed_window - Keep the window size fixed when in single-scale mode (multi-scale always used a fixed window).   
show - Show the results in a window.   

To include it in your project, without the VOT toolkit you just need to:
	
	// Create the KCFTracker object with one of the available options
	KCFTracker tracker(HOG, FIXEDWINDOW, MULTISCALE, LAB);

	// Give the first frame and the position of the object to the tracker
	tracker.init( Rect(xMin, yMin, width, height), frame );

	// Get the position of the object for the new frame
	result = tracker.update(frame);