This is a fork of the Python implmentation of KCF. I have modified the code to run sequences from the VOT Challenge and provide Accuracy, Robustness and Frames Per Second evaluation metrics.

Example from VOT2016/matrix sequence:

The original paper:

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

• Python 2.7
• NumPy
• Numba
• OpenCV (ensure that you can import cv2 in python)

I recommend installing Anaconda(for Python 2.7), and OpenCV 3.1(from opencv.org).

• Install the required packages

git clone https://github.com/uoip/KCFpy.git
cd KCFpy

• Download a dataset from the VOT Challenge. webpage. eg. VOT2016 dataset
• copy the dataset into the 'VOTdataset' folder in KCFpy
• from the command window, call run.py with 1 paramater: The path to the sequence you wish to run from the 'VOTdataset' folder
  eg:

python run.py VOT2016/matrix

The figures will automatically be saved into the 'saved' folder. Comment out line 192 in run.py to stop this.

I used Anaconda for Python 3 and setup a python 2.7 virtual environment called 'pykcf' on Windows 10.

How I did this:

conda create --name pykcf python=2.7.13 numpy numba scipy
activate pykcf
pip install openCV-python