FFO

Code for A fusion framework for vision-based indoor occupancy estimation.

Paper

Environment

• The code is tested on Ubuntu 20.04.2, python 3.8, cuda 11.1.

Installation

1. Install pytorch

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html

2. Clone this repository

git clone https://github.com/kailaisun/FFO

3. Install

pip install -r requirements.txt

Test

SCM ( Scene-based counting method)

python people_detect.py --path <video_path>

• Result of SCM

• You can modify hyperparameters of JointDet module in person_detect.py.

 result_info = joint_de(head_info, other_info,thresh=0.8,conf=0.6,thresh1=0.8)  #line 50

LCM ( Line-based counting method)

• peopeo_count.py conducts LCM (YOLOX+Deepsort) of indoor view.
• After you obtained the sequences of two-vision LCM:

python joint.py

• Note that in overhead entrance counting method our video frame rate is downsampled to one-fifth of the original video.
• Result of YOLOX+Deepsort

label of indoor view LCM
```
frame: i, in/out num: y
frame: k, in/out num: y
.
.
.
```
label of overhead view LCM
```
frame: i, num: y
frame: i+1, num: y
frame: i+2, num: y
.
.
.
```

DBF

pytho main.py

Citation

Please refer to the following bibtex to cite.

@article{SUN2022109631,
title = {A fusion framework for vision-based indoor occupancy estimation},
        journal = {Building and Environment},
        volume = {225},
        pages = {109631},
        year = {2022},
        issn = {0360-1323},
        doi = {https://doi.org/10.1016/j.buildenv.2022.109631},
        author = {Kailai Sun and Peng Liu and Tian Xing and Qianchuan Zhao and Xinwei Wang}
}

Contact Us

If you have other questions❓, please contact us in time 👬