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

Related tags

Deep LearningHDN
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.

Owner
CaptainHook
CaptainHook
GitHub Repository
TLXZoo - Pre-trained models based on TensorLayerX

Pre-trained models based on TensorLayerX. TensorLayerX is a multi-backend AI fra

TensorLayer Community 13 Dec 07, 2022
Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size.

Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size. The hub data layout enables rapid transformations and streaming of data while training m

Activeloop 5.1k Jan 08, 2023
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

Subspace Adversarial Training Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However,

15 Sep 02, 2022
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)

[NeurIPS 2021 Spotlight] HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning [Paper] This is Official PyTorch implementatio

42 Nov 01, 2022
Code for the paper Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration

IMAGINE: Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration This repo contains the code base of the paper Language as a Cog

Flowers Team 26 Dec 22, 2022
Optimizaciones incrementales al problema N-Body con el fin de evaluar y comparar las prestaciones de los traductores de Python en el ámbito de HPC.

Python HPC Optimizaciones incrementales de N-Body (all-pairs) con el fin de evaluar y comparar las prestaciones de los traductores de Python en el ámb

Andrés Milla 12 Aug 04, 2022
FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well i

0 Sep 06, 2022
Flexible-CLmser: Regularized Feedback Connections for Biomedical Image Segmentation

Flexible-CLmser: Regularized Feedback Connections for Biomedical Image Segmentation The skip connections in U-Net pass features from the levels of enc

Boheng Cao 1 Dec 29, 2021
Alfred-Restore-Iterm-Arrangement - An Alfred workflow to restore iTerm2 window Arrangements

Alfred-Restore-Iterm-Arrangement This alfred workflow will list avaliable iTerm2

7 May 10, 2022
Does Pretraining for Summarization Reuqire Knowledge Transfer?

Pretraining summarization models using a corpus of nonsense

Approximately Correct Machine Intelligence (ACMI) Lab 12 Dec 19, 2022
Implementation for Curriculum DeepSDF

Curriculum-DeepSDF This repository is an implementation for Curriculum DeepSDF. Full paper is available here. Preparation Please follow original setti

Haidong Zhu 69 Dec 29, 2022
[ECCVW2020] Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DiMP)

Feel free to visit my homepage Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DIMP) [ECCVW2020 paper] Presentation

Seokeon Choi 35 Oct 26, 2022
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021]

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps Here is the code for ssbassline model. We also provide OCR results/features/mode

ZephyrZhuQi 51 Nov 18, 2022
Visual Adversarial Imitation Learning using Variational Models (VMAIL)

Visual Adversarial Imitation Learning using Variational Models (VMAIL) This is the official implementation of the NeurIPS 2021 paper. Project website

14 Nov 18, 2022
A collection of inference modules for fastai2

fastinference A collection of inference modules for fastai including inference speedup and interpretability Install pip install fastinference There ar

Zachary Mueller 83 Oct 10, 2022
GraPE is a Rust/Python library for high-performance Graph Processing and Embedding.

GraPE GraPE (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both of

AnacletoLab 194 Dec 29, 2022
COCO Style Dataset Generator GUI

A simple GUI-based COCO-style JSON Polygon masks' annotation tool to facilitate quick and efficient crowd-sourced generation of annotation masks and bounding boxes. Optionally, one could choose to us

Hans Krupakar 142 Dec 09, 2022
The PyTorch implementation of DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision.

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision The PyTorch implementation of DiscoBox: Weakly Supe

Shiyi Lan 1 Oct 23, 2021
Neural Re-rendering for Full-frame Video Stabilization

NeRViS: Neural Re-rendering for Full-frame Video Stabilization Project Page | Video | Paper | Google Colab Setup Setup environment for [Yu and Ramamoo

Yu-Lun Liu 9 Jun 17, 2022
A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data

A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data Overview Clustering analysis is widely utilized in single-cell RNA-seque

AI-Biomed @NSCC-gz 3 May 08, 2022