PyTorch implementation of HDN(Homography Decomposition Networks) for planar object tracking

CaptainHook

Last update: Dec 15, 2022

Related tags

Deep Learning HDN

Overview

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.

You might also like...

This package is for running the semantic SLAM algorithm using extracted planar surfaces from the received detection

Semantic SLAM This package can perform optimization of pose estimated from VO/VIO methods which tend to drift over time. It uses planar surfaces extra

125 Dec 2, 2022

A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

117 Dec 28, 2022

Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

SiamMOT is a region-based Siamese Multi-Object Tracking network that detects and associates object instances simultaneously.

SiamMOT: Siamese Multi-Object Tracking

432 Dec 17, 2022

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction TSDF++ is a novel multi-object TSDF formulation that can encode mult

130 Dec 29, 2022

Comments

can not download Demo video

Dear Master,

I want to check Demo video through attached link (https://www.aliyundrive.com/s/ycDqPLz5e3Z/folder/61bef2d66bbb3e8803544251adf525354f3240bd) But, I don't know how to download it.... Could you please share Demo video through Google Drive?

Thanks, kwanghyuk

opened by kwanghyukAn 1
Fine Tuning The Model

I am trying to fine-tune the HDN model on the POT dataset. I have preprocessed the POT dataset as instructed. However, the training loss seems jumpy as it fluctuates from 1 to 600. I tried to debug the training flow and found that it happens when we feed the supervised training samples to the model. Also, the loss for the HMNET model comes out to be nan. Even after training the model for 1 epoch, the model performance decreases too much. Is there a bug in the training pipeline? Please assist.

opened by SHANK885 1

PyTorch implementation of HDN(Homography Decomposition Networks) for planar object tracking

Related tags

Overview

Homography Decomposition Networks for Planar Object Tracking

Project Page | Paper

Installation

Quick Start: Using HDN

Add HDN to your PYTHONPATH

Download models

Base Setting

Demo

Download testing datasets

Test tracker

Eval tracker

For POT evaluation

For others:

Training 🔧

Acknowledgement

License

You might also like...

This package is for running the semantic SLAM algorithm using extracted planar surfaces from the received detection

A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

[ICCV 2021 (oral)] Planar Surface Reconstruction from Sparse Views

Code for "Unsupervised Layered Image Decomposition into Object Prototypes" paper

MEND: Model Editing Networks using Gradient Decomposition

Object Detection and Multi-Object Tracking

SiamMOT is a region-based Siamese Multi-Object Tracking network that detects and associates object instances simultaneously.

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

Comments

can not download Demo video

Fine Tuning The Model

Owner

CaptainHook

Python and C++ implementation of "MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation". Accepted at LXCV @ CVPR 2021.

Code for Blind Image Decomposition (BID) and Blind Image Decomposition network (BIDeN).

Implement of homography net by pytorch

A numpy-based implementation of RANSAC for fundamental matrix and homography estimation. The degeneracy updating and local optimization components are included and optional.

Official PyTorch implementation of Joint Object Detection and Multi-Object Tracking with Graph Neural Networks

Code for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in Deep Latent Space"

Pytorch implementation of paper: "NeurMiPs: Neural Mixture of Planar Experts for View Synthesis"

Python package for multiple object tracking research with focus on laboratory animals tracking.

git《Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction》(ECCV 2020) GitHub:

Planar Prior Assisted PatchMatch Multi-View Stereo