Homography Decomposition Networks for Planar Object Tracking
This project is the offical PyTorch implementation of HDN(Homography Decomposition Networks) for planar object tracking. (AAAI 2022, Accepted)
Project Page | Paper
@misc{zhan2021homography,
title={Homography Decomposition Networks for Planar Object Tracking},
author={Xinrui Zhan and Yueran Liu and Jianke Zhu and Yang Li},
year={2021},
eprint={2112.07909},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Installation
Please find installation instructions in INSTALL.md.
Quick Start: Using HDN
Add HDN to your PYTHONPATH
vim ~/.bashrc
# add home of project to PYTHONPATH
export PYTHONPATH=/path/to/HDN:/path/to/HDN/homo_estimator/Deep_homography/Oneline_DLTv1:$PYTHONPATH
Download models
Google Drive or Baidu Netdisk (key: 8uhq)
Base Setting
The global parameters setting file is hdn/core/config.py You first need to set the base path:
__C.BASE.PROJ_PATH = /xxx/xxx/project_root/ #/home/Kay/SOT/server_86/HDN/ (path_to_hdn)
__C.BASE.BASE_PATH = /xxx/xxx/ #/home/Kay/SOT/ (base_path_to_workspace)
__C.BASE.DATA_PATH = /xxx/xxx/data/POT #/home/Kay/data/POT (path to POT datasets)
__C.BASE.DATA_ROOT = /xxx/xxx #/home/Kay/Data/Dataset/ (path to other datasets)
Demo
Planar Object Tracking and its applications we provide 4 modes:
- tracking: tracking planar object with not less than 4 points in the object.
- img_replace: replacing planar object with image .
- video_replace: replacing planar object with video.
- mosiac: adding mosiac to planar object.
python tools/demo.py
--snapshot model/hdn-simi-sup-hm-unsup.pth
--config experiments/tracker_homo_config/proj_e2e_GOT_unconstrained_v2.yaml
--video demo/door.mp4
--mode img_replace
--img_insert demo/coke2.jpg #required in mode 'img_replace'
--video_insert demo/t5_videos/replace-video/ #required in mode 'video_replace'
--save # whether save the results.
e.g.
python tools/demo.py --snapshot model/hdn-simi-sup-hm-unsup.pth --config experiments/tracker_homo_config/proj_e2e_GOT_unconstrained_v2.yaml --video demo/door.mp4 --mode img_replace --img_insert demo/coke2.jpg --save
we provide some real-world videos here
Download testing datasets
POT
For POT dataset, download the videos from POT280 and annotations from here
1. unzip POT_v.zip and POT_annotation.zip and put them in your cfg.BASE.DATA_PATH #unzip the zip files
cd POT_v
unzip "*.zip"
cd ..
2. mkdir POT
mkdir path_to_hdn/testing_dataset
python path_to_hdn/toolkit/benchmarks/POT/pot_video_to_pic.py #video to images
ln -s path_to_data/POT path_to_hdn/testing_dataset/POT #link to testing_datasets
4. python path_to_hdn/toolkit/benchmarks/POT/generate_json_for_POT.py --dataset POT210 #generate json annotation for POT
python path_to_hdn/toolkit/benchmarks/POT/generate_json_for_POT.py --dataset POT280
UCSB & POIC
Download from here put them in your cfg.BASE.DATA_PATH
ln -s path_to_data/UCSB path_to_hdn/testing_dataset/UCSB #link to testing_datasets
generate json:
python path_to_hdn/toolkit/benchmarks/POIC/generate_json_for_poic.py #generate json annotation for POT
python path_to_hdn/toolkit/benchmarks/UCSB/generate_json_for_ucsb.py #generate json annotation for POT
Other datsets:
Download datasets and put them into testing_dataset directory. Jsons of commonly used datasets can be downloaded from here. If you want to test tracker on new dataset, please refer to pysot-toolkit to setting testing_dataset.
Test tracker
- test POT
cd experiments/tracker_homo_config
python -u ../../tools/test.py \
--snapshot ../../model/hdn-simi-sup-hm-unsup.pth \ # model path
--dataset POT210 \ # dataset name
--config proj_e2e_GOT_unconstrained_v2.yaml # config file
--vis #display video
The testing results will in the current directory(./results/dataset/model_name/)
Eval tracker
For POT evaluation
1.use tools/change_pot_results_name.py to convert result_name(you need to set the path in the file).
2.use tools/convert2Homography.py to generate the homo file(you need to set the corresponding path in the file).
3.use POT toolkit to test the results. My version toolkit can be found here or official for other trackers:
For others:
For POIC, UCSB or POT evaluation on centroid precision, success rate, and robustness etc. assuming still in experiments/tracker_homo_config
python ../../tools/eval.py \
--tracker_path ./results \ # result path
--dataset POIC \ # dataset name
--num 1 \ # number thread to eval
--tracker_prefix 'model' # tracker_name
The raw results can be downloaded at Google Drive or Baidu Netdisk (key:d98h)
Training
🔧
We use the COCO14 and GOT10K as our traning datasets. See TRAIN.md for detailed instruction.
Acknowledgement
This work is supported by the National Natural Science Foundation of China under Grants (61831015 and 62102152) and sponsored by CAAI-Huawei MindSpore Open Fund.
Our codes is based on SiamBAN and DeepHomography.
License
This project is released under the Apache 2.0 license.
227 Dec 28, 2022
14 Apr 07, 2022
1 Aug 09, 2022
336 Dec 30, 2022
10 Oct 25, 2022
3 Nov 18, 2021
2 Mar 16, 2022
127 Dec 16, 2022
3 Oct 09, 2022
422 Jan 04, 2023
121 Dec 25, 2022
46 Nov 17, 2022
7 Oct 13, 2022
47 Dec 30, 2022
53 Oct 27, 2022
118 Dec 01, 2022
58 Dec 30, 2022
0 Aug 22, 2021
81 Dec 15, 2022
35 Dec 06, 2022