/ Machine Learning

Python re-implementation of some correlation filter based tracker

Python re-implementation of some correlation filter based tracker

pyCFTrackers

Python re-implementation of some correlation filter based tracker, and all of these algorithms are implemented based on the official Matlab code. All the code has been tested on Ubuntu 16.04, Python 3.5. I use pysot-toolkit to eval the performance on OTB and VOT.

Install

git clone https://github.com/wwdguu/pyCFTrackers.git && cd pyCFTrackers
export pyCFTrackers=$PWD

pip install -r requirements.txt

cd lib/eco/features/
python setup.py build_ext --inplace
cd ../../..

cd lib/pysot/utils/
python setup.py build_ext --inplace
cd ../../..

export PYTHONPATH=$PWD:$PYTHONPATH

Get Dataset

You can follow the instructions in the following repo to get VOT2016,VOT2018 and OTB100 dataset.
trackdat
Then get the json files according to pysot-toolkit
Then put the data in the dataset dir.

Demo

cd examples
python cf_demo.py

Coke_vis

Eval on OTB and VOT

cd eval
python get_vot2016_result.py
python get_vot2018_result.py
python ope_otb.py
python eval_VOT2016.py
python  eval_VOT2018.py
python eval_OTB.py

OTB result

OTB-100

OTB100 Success Plot OTB100 Precision Plot
pytracker_OPE_OTB100_success pytracker_OPE_OTB100_precision

OTB-2013

OTB2013 Success Plot OTB2013 Precision Plot
pytracker_OPE_OTB2013_success pytracker_OPE_OTB2013_precision

VOT result

VOT2018
---------------------------------------------------------------
| Tracker Name  | Accuracy | Robustness | Lost Number |  EAO  |
---------------------------------------------------------------
|      ECO      |  0.485   |   0.403    |    86.0     | 0.224 |
|    CSRDCF     |  0.492   |   0.501    |    107.0    | 0.210 |
|    ECO-HC     |  0.500   |   0.473    |    101.0    | 0.207 |
|   CSRDCF-LP   |  0.503   |   0.553    |    118.0    | 0.199 |
|    Staple     |  0.524   |   0.665    |    142.0    | 0.179 |
|     LDES      |  0.528   |   0.684    |    146.0    | 0.175 |
| MCCTH-Staple  |  0.535   |   0.684    |    146.0    | 0.172 |
| OPENCV-CSRDCF |  0.486   |   0.651    |    139.0    | 0.170 |
|     BACF      |  0.511   |   0.674    |    144.0    | 0.169 |
|      DAT      |  0.477   |   0.777    |    166.0    | 0.158 |
|     STRCF     |  0.483   |   0.679    |    145.0    | 0.152 |
|      CN       |  0.439   |   1.100    |    235.0    | 0.112 |
|     SAMF      |  0.499   |   1.147    |    245.0    | 0.110 |
|     DSST      |  0.492   |   1.222    |    261.0    | 0.107 |
|    DSST-LP    |  0.512   |   1.260    |    269.0    | 0.103 |
|      DCF      |  0.463   |   1.246    |    266.0    | 0.099 |
|      KCF      |  0.463   |   1.330    |    284.0    | 0.094 |
|      CSK      |  0.418   |   1.386    |    296.0    | 0.090 |
|     MOSSE     |  0.378   |   1.967    |    420.0    | 0.063 |
---------------------------------------------------------------

VOT2016
---------------------------------------------------------------
| Tracker Name  | Accuracy | Robustness | Lost Number |  EAO  |
---------------------------------------------------------------
|      ECO      |  0.564   |   0.256    |    55.0     | 0.336 |
| MCCTH-Staple  |  0.574   |   0.359    |    77.0     | 0.303 |
|    Staple     |  0.560   |   0.387    |    83.0     | 0.299 |
|    ECO-HC     |  0.532   |   0.350    |    75.0     | 0.293 |
|    CSRDCF     |  0.542   |   0.359    |    77.0     | 0.273 |
|     LDES      |  0.577   |   0.419    |    90.0     | 0.272 |
|   CSRDCF-LP   |  0.548   |   0.354    |    76.0     | 0.272 |
|     BACF      |  0.521   |   0.405    |    87.0     | 0.252 |
| OPENCV-CSRDCF |  0.521   |   0.438    |    94.0     | 0.239 |
|     STRCF     |  0.520   |   0.415    |    89.0     | 0.239 |
|      DAT      |  0.474   |   0.503    |    108.0    | 0.232 |
|     SAMF      |  0.544   |   0.639    |    137.0    | 0.193 |
|    DSST-LP    |  0.543   |   0.727    |    156.0    | 0.180 |
|      CN       |  0.468   |   0.653    |    140.0    | 0.178 |
|     DSST      |  0.531   |   0.732    |    157.0    | 0.177 |
|      DCF      |  0.474   |   0.704    |    151.0    | 0.171 |
|      KCF      |  0.469   |   0.718    |    154.0    | 0.171 |
|      CSK      |  0.433   |   0.886    |    190.0    | 0.139 |
|     MOSSE     |  0.388   |   1.244    |    267.0    | 0.096 |
---------------------------------------------------------------

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