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DyGLIP: A dynamic graph model with link prediction for accurate multi-camera multiple object tracking
Authors: Kha Gia Quach, Pha Nguyen, Huu Le, Thanh-Dat Truong, Chi Nhan Duong, Minh-Triet Tran, Khoa Luu
Email: kquach@ieee.org, panguyen@uark.edu
Overview
We release the code for our paper in CVPR 2021. For more information please refer to our accepted paper in CVPR 2021.
Project Download
Firstly please download the project through:
git clone https://github.com/uark-cviu/DyGLIP
Prerequisites
The code requires the following libraries to be installed:
- [Python] (https://python.org/) 3.6+
- PyTorch >= 1.5
- [CUDA] (https://developer.nvidia.com/cuda-downloads) 9.0+
cd DyGLIP
conda env create -f environment.yml
conda activate dyglip
Data Preparation
Please place all datasets in /data/
:
/data/
├── CAMPUS
│ ├── Auditorium
│ ├── Garden1
│ ├── Garden2
│ └── Parkinglot
├── EPFL
│ ├── Basketball
│ ├── Campus
│ ├── Laboratory
│ ├── Passageway
│ └── Terrace
├── PETS09
├── MCT
│ ├── Dataset1
│ ├── Dataset2
│ ├── Dataset3
│ └── Dataset4
└── aic
├── S02
└── S05
Step 1 - Detection
Please follow detection guidance to get bounding boxes prediction.
Get maskrcnn features by running the file Step1_Detection/identifier/preprocess/extract_img_and_feat.py
, the output should be two files: bboxes.pkl
and maskrcnn_feats.pkl
.
Get pre-computed reid features by running the file Step1_Detection/identifier/preprocess/extract_img_and_reid_feat.py
, the output should be the reid_feats.pkl
file.
Step 2 - Graph-based Feature Extraction
This environment must be python 2.7, tensorflow 1.11
Prepare graph Step2_GraphFeature/prepare_graphs.py
Please follow GraphFeature guidance to train the model.
Step 3 - Matching
Get output from matching baselines graph and non-negative matrix factorization.
Acknowledgements
- Thanks DySAT for providing strong baseline for graph attention network.
- Thanks ELECTRICITY-MTMC for providing useful detection inference pipeline for MC-MOT.
Citation
If you find this code useful for your research, please consider citing:
@InProceedings{Quach_2021_CVPR,
author = {Quach, Kha Gia and Nguyen, Pha and Le, Huu and Truong, Thanh-Dat and Duong, Chi Nhan and Tran, Minh-Triet and Luu, Khoa},
title = {DyGLIP: A Dynamic Graph Model With Link Prediction for Accurate Multi-Camera Multiple Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {13784-13793}
}