Introduction

CMT (Consensus-based Matching and Tracking of Keypoints for Object Tracking) is a novel keypoint-based method for long-term model-free object tracking in a combined matching-and-tracking framework. Details can be found on the project page and in our publication.

If you use our algorithm in scientific work, please cite our publication

@inproceedings{Nebehay2014WACV,
    author = {Nebehay, Georg and Pflugfelder, Roman},
    booktitle = {Winter Conference on Applications of Computer Vision},
    month = mar,
    publisher = {IEEE},
    title = {Consensus-based Matching and Tracking of Keypoints for Object Tracking},
    year = {2014}
}

Dependencies

Python
OpenCV-Python (>= 2.4)
NumPy
SciPy

Usage

usage: run.py [-h] [--challenge] [--preview] [--no-preview] [--no-scale]
               [--no-rotation] [--bbox BBOX] [--pause] [--output-dir OUTPUT]
               [--quiet]
               [inputpath]

Optional arguments

inputpath The input path.
-h, --help show help message and exit
--challenge Enter challenge mode.
--preview Force preview
--no-preview Disable preview
--no-scale Disable scale estimation
--with-rotation Enable rotation estimation
--bbox BBOX Specify initial bounding box. Format: x,y,w,h
--pause Pause after each frame
--output-dir OUTPUT Specify a directory for output data.
--quiet Do not show graphical output (Useful in combination with --output-dir).

Object Selection

Press any key to stop the preview stream. Left click to select the top left bounding box corner and left click again to select the bottom right corner.

Examples

When using a webcam, no arguments are necessary:

python run.py

When using a video, the path to the file has to be given as an input parameter:

python run.py /home/cmt/test.avi

It is also possible to specify the initial bounding box on the command line.

python run.py --bbox=123,85,60,140 /home/cmt/test.avi