Awesome

Unsupervised Single Object Tracking (USOT)

:herb: Learning to Track Objects from Unlabeled Videos

Jilai Zheng, Chao Ma, Houwen Peng and Xiaokang Yang

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Introduction

This repository implements unsupervised deep tracker USOT, which learns to track objects from unlabeled videos.

Main ideas of USOT are listed as follows.

Coarsely discovering moving objects from videos, with pseudo boxes precise enough for bbox regression.
Training a naive Siamese tracker from single-frame pairs, then gradually extending it to longer temporal spans.
Following cycle memory training paradigm, enabling unsupervised tracker to update online.

Results

Results of USOT and USOT* on recent tracking benchmarks.

<table> <tr> <th>Model</th> <th>VOT2016<br> EAO </th> <th>VOT2018<br> EAO </th> <th>VOT2020<br> EAO </th> <th>LaSOT<br> AUC (%)</th> <th>TrackingNet<br> AUC (%)</th> <th>OTB100<br> AUC (%)</th> <th>GOT10k<br> AO (%)</th> </tr> <tr> <td>USOT</td> <td>0.351</td> <td>0.290</td> <td>0.222</td> <td>33.7</td> <td>59.9</td> <td>58.9</td> <td>0.444</td> </tr> <tr> <td>USOT*</td> <td>0.402</td> <td>0.344</td> <td>0.219</td> <td>35.8</td> <td>61.5</td> <td>57.4</td> <td>0.441</td> </tr> </table>

Raw result files can be found in folder result from Google Drive.

Tutorial

Environments

The environment we utilize is listed as follows.

Preprocessing: Pytorch 1.1.0 + CUDA-9.0 / 10.0 (following ARFlow)
Train / Test / Eval: Pytorch 1.7.1 + CUDA-10.0 / 10.2 / 11.1

If you have problems for preprocessing, you can actually skip it by downloading off-the-shelf preprocessed materials.

Preparations

Assume the project root path is $USOT_PATH. You can build an environment for development with the provided script, where $CONDA_PATH denotes your anaconda path.

cd $USOT_PATH
bash ./preprocessing/install_model.sh $CONDA_PATH USOT
source activate USOT && export PYTHONPATH=$(pwd)

You can revise the CUDA toolkit version for pytorch in install_model.sh (by default 10.0).

Test and Eval

First, we provide both models utilized in our paper (USOT.pth and USOT_star.pth). You can download them in folder snapshot from Google Drive, and place them in $USOT_PATH/var/snapshot.

Next, you can link your wanted benchmark dataset (e.g. VOT2018) to $USOT_PATH/datasets_test as follows. The ground truth json files for some benchmarks (e.g VOT2018.json) can be downloaded in folder test from Google Drive, and placed also in $USOT_PATH/datasets_test.

cd $USOT_PATH && mkdir datasets_test
ln -s $your_benchmark_path ./datasets_test/VOT2018

After that, you can test the tracker on these benchmarks (e.g. VOT2018) as follows. The raw results will be placed in $USOT_PATH/var/result/VOT2018/USOT.

cd $USOT_PATH
python -u ./scripts/test_usot.py --dataset VOT2018 --resume ./var/snapshot/USOT_star.pth

The inference result can be evaluated with pysot-toolkit. Install pysot-toolkit before evaluation.

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

Then the evaluation can be conducted as follows.

cd $USOT_PATH
python ./lib/eval_toolkit/bin/eval.py --dataset_dir datasets_test \
        --dataset VOT2018 --tracker_result_dir var/result/VOT2018 --trackers USOT

Train

First, download the pretrained backbone in folder pretrain from Google Drive into $USOT_PATH/pretrain. Note that USOT* and USOT are respectively trained from imagenet_pretrain.model and moco_v2_800.model.

Second, preprocess the raw datasets with the paradigm of DP + Flow. Refer to $USOT_PATH/preprocessing/datasets_train for details.

In fact, we have provided two shortcuts for skipping this preprocessing procedure.

You can directly download the generated pseudo box files (e.g. got10k_flow.json) in folder train/box_sample_result from Google Drive, and place them into the corresponding dataset preprocessing path (e.g. $USOT_PATH/preprocessing/datasets_train/got10k), in order to skip the box generation procedure.
You can directly download the whole cropped training dataset (e.g. got10k_flow.tar) in dataset folder from Baidu Drive (6887) (e.g. train/GOT-10k), which enables you to skip all procedures in preprocessing.

Third, revise the config file for training as $USOT_PATH/experiments/train/USOT.yaml. Very important options are listed as follows.

GPUS: the gpus for training, e.g. '0,1,2,3'
TRAIN/PRETRAIN: the pretrained backbone, e.g. 'imagenet_pretrain.model'
DATASET: the folder for your cropped training instances and their pseudo annotation files, e.g. PATH: '/data/got10k_flow/crop511/', ANNOTATION: '/data/got10k_flow/train.json'

Finally, you can start the training phase with the following script. The training checkpoints will also be placed automatically in $USOT_PATH/var/snapshot.

cd $USOT_PATH
python -u ./scripts/train_usot.py --cfg experiments/train/USOT.yaml --gpus 0,1,2,3 --workers 32

We also provide a onekey script for train, test and eval.

cd $USOT_PATH
python ./scripts/onekey_usot.py --cfg experiments/train/USOT.yaml

Citation

If any parts of our paper and codes are helpful to your work, please generously citing:

@inproceedings{zheng-iccv2021-usot,
   title={Learning to Track Objects from Unlabeled Videos},
   author={Jilai Zheng and Chao Ma and Houwen Peng and Xiaokang Yang},
   booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
   year={2021}
}

Reference

We refer to the following repositories when implementing our unsupervised tracker. Thanks for their great work.

Contact

Feel free to contact me if you have any questions.

Jilai Zheng, email: zhengjilai@sjtu.edu.cn