KCF-DSST-fDSST-SAMF代码（python+matlab版）

# 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](https://github.com/StrangerZhang/pysot-toolkit) to eval the performance on OTB and VOT. - [x] [MOSSE](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.4992&rep=rep1&type=pdf) - [x] [CSK](http://59.80.44.48/www.robots.ox.ac.uk/~joao/publications/henriques_eccv2012.pdf) - [x] [CN](http://117.128.6.12/cache/www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/CN_Tracking_CVPR14.pdf?ich_args2=465-31142901008185_f9df5d61efad793a151f3e0f467d3f75_10001002_9c896128d7c2f2d6933d518939a83798_91ccc5b03febd95ae516eb0f69b18b49) - [x] [KCF/DCF](http://www.robots.ox.ac.uk/~joao/publications/henriques_tpami2015.pdf) - [x] [DSST/DSST-LP](http://www.cvl.isy.liu.se/research/objrec/visualtracking/scalvistrack/ScaleTracking_BMVC14.pdf) - [x] [SAMF](https://link.springer.com/content/pdf/10.1007%2F978-3-319-16181-5_18.pdf) - [x] [Staple](https://arxiv.org/pdf/1512.01355v2.pdf) - [x] [Staple-CA](https://ivul.kaust.edu.sa/Documents/Publications/2017/Context-Aware%20Correlation%20Filter%20Tracking.pdf) - [x] [BACF](http://openaccess.thecvf.com/content_ICCV_2017/papers/Galoogahi_Learning_Background-Aware_Correlation_ICCV_2017_paper.pdf) - [x] [CSR-DCF/CSR-DCF-LP](https://arxiv.org/pdf/1611.08461v1.pdf) - [x] [DAT](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/dat/) This tracker is not based on CF, I re-implement it just for understanding Staple better - [x] [LDES](https://arxiv.org/pdf/1712.05231.pdf) - [x] [MKCFup/MKCFup-LP](http://openaccess.thecvf.com/content_cvpr_2018/papers/Tang_High-Speed_Tracking_With_CVPR_2018_paper.pdf) - [x] [MCCTH](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Multi-Cue_Correlation_Filters_CVPR_2018_paper.pdf) - [x] [STRCF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Learning_Spatial-Temporal_Regularized_CVPR_2018_paper.pdf) **Trackers from other repos** - [x] [ECO/ECO-HC](https://arxiv.org/pdf/1611.09224v1.pdf) from [pyECO](https://github.com/StrangerZhang/pyECO) - [x] [OPENCV-CSRDCF](https://github.com/opencv/opencv_contrib)(Note that trackers implemented in opencv get lower score because they will return a success flag and if this flag is False, the bbox will be set to (0,0,0,0)) ## Install ``` bash 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](https://github.com/jvlmdr/trackdat/tree/master/python/trackdat) Then get the json files according to [pysot-toolkit](https://github.com/StrangerZhang/pysot-toolkit) Then put the data in the dataset dir. ## Demo ``` bash cd examples python cf_demo.py ```  ## Eval on OTB and VOT ``` bash 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 | | --------------------------------- | ----------------------------- | | | | ### OTB-2013 | OTB2013 Success Plot | OTB2013 Precision Plot | | --------------------------------- | ----------------------------- | | | | ## 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 | --------------------------------------------------------------- ``` ## License. Licensed under an MIT license.