ObjectTracker

This is a fork of the C++ implementation of OpenTLD called TrackTLD. OpenTLD was originally proposed in [1] and implemented by Georg Nebehay in C++.

This fork uses C++ implementations of correlation filter based trackers as short-term trackers. Both short-term trackers are modified variants of the visual trackers proposed in [2,3]. The short-term trackers are extended with target loss detection capabilities as in [4] and use the C++ implementation [5] of the FHOG features proposed in [6]. The detection cascade is only used to suggest possible target locations to the short-term trackers for redetection purposes. It cannot reinitialize the short-term trackers. The short-term trackers decide whether a suggested patch actually contains the target.

The system starts with the KCFcpp as default short-term tracker.

Usage

If you have a webcam attached to your ROCK960, you can simply execute cftld in order to try it out.

Press 'r' key, drag the mouse to draw the box to be tracked target. And press the Enter key to start tracking.

Keyboard shortcuts

r select a target
q quit
l toggle learning
a toggle alternating mode (if true, detector is switched off when tracker is available)

Documents

Release History

Version 0.1.3 - 2018-02-05

Add script to test accuracy.
Add 2xA72 and 4xA53 performance data.

Commercial Use (US)

The code using linear correlation filters may be affected by a US patent. If you want to use this code commercially in the US please refer to http://www.cs.colostate.edu/~vision/ocof_toolset_2012/index.php for possible patent claims.

References

If you reuse this code for a scientific publication, please cite the related publications (dependent on what parts of the code you reuse):

[1]

@article{kalal2012TLD,
title={Tracking-Learning-Detection},
author={Kalal, Zdenek and Mikolajczyk, Krystian and Matas, Jiri},
journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on},
year={2012}}

[2]

@article{henriques2015tracking,
title = {High-Speed Tracking with Kernelized Correlation Filters},
author = {Henriques, J. F. and Caseiro, R. and Martins, P. and Batista, J.},
journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
year = {2015}

[3]

@inproceedings{danelljan2014dsst,
title={Accurate Scale Estimation for Robust Visual Tracking},
author={Danelljan, Martin and H{\"a}ger, Gustav and Khan, Fahad Shahbaz and Felsberg, Michael},
booktitle={Proceedings of the British Machine Vision Conference BMVC},
year={2014}}

[4]

@inproceedings{bolme2010mosse,
author={Bolme, David S. and Beveridge, J. Ross and Draper, Bruce A. and Yui Man Lui},
title={Visual Object Tracking using Adaptive Correlation Filters},
booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2010}}

[5]

@misc{PMT,
author = {Piotr Doll\'ar},
title = {{P}iotr's {C}omputer {V}ision {M}atlab {T}oolbox ({PMT})},
howpublished = {\url{http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html}}}

[6]

@article{lsvm-pami,
title = "Object Detection with Discriminatively Trained Part Based Models",
author = "Felzenszwalb, P. F. and Girshick, R. B. and McAllester, D. and Ramanan, D.",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
year = "2010", volume = "32", number = "9", pages = "1627--1645"}

OAID / ObjectTracker