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https://github.com/JunweiLiang/social-distancing-prediction
Out-of-the-box code and models for social distancing early forecasting.
https://github.com/JunweiLiang/social-distancing-prediction
activity-prediction object-tracking social-distancing trajectory-prediction
Last synced: about 11 hours ago
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Out-of-the-box code and models for social distancing early forecasting.
- Host: GitHub
- URL: https://github.com/JunweiLiang/social-distancing-prediction
- Owner: JunweiLiang
- License: apache-2.0
- Created: 2020-04-04T19:50:35.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-04-06T05:06:00.000Z (over 1 year ago)
- Last Synced: 2024-11-08T15:08:46.078Z (2 days ago)
- Topics: activity-prediction, object-tracking, social-distancing, trajectory-prediction
- Language: Python
- Homepage: https://medium.com/@junweil/social-distancing-early-forecasting-system-60186baa67f5
- Size: 101 MB
- Stars: 42
- Watchers: 5
- Forks: 21
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Social Distancing Early Forecasting System
Out-of-the-box code base for social distancing early forecasting. Given a video, this code will give out a warning if the system predicts that people will violate social distancing (getting closer with each other than 6 feet) in the next 5 seconds. This early warnings could help stop people before they are actually at risk of getting infected. See this [blog](https://medium.com/@junweil/social-distancing-early-forecasting-system-60186baa67f5).
Keep social distancing (about 6 feet) and [save lives](https://www.cdc.gov/)!
Below we show an example of the system output. If potential risks are detected, trajectory predictions are shown and warnings are printed near the person.
## Dependencies
+ Python 2/3; TensorFlow-GPU==1.15.2; cv2; tqdm; scipy; sklearn; matplotlib; ffmpeg
## Usage
### Step 1: Download models and a test video
Assuming you run the code at the top level of this repository. Model size is about 468MB and the test video is about 7MB.
```
bash scripts/download_models.sh
bash scripts/download_test_video.sh
```
### Step 2: Run inferencing
```
python code/inference/main.py test/test_videos.lst test/output --pred_vis_path test/visualization
```
### Step 3: Make a video
```
cd test/visualization
ffmpeg -framerate 30.0 -i test_video/test_video_F_%08d.jpg test_video.mp4
```
## Speed
My limited tests show that on a RTX 2060 (6GB memory) the processing time is 2x real-time, which means a one-minute 1920x1080 video will take 2 minute to process.
On a GTX 1080 TI it is about 1x real-time.
Reducing input resolution will significantly decrease the processing time.
The visualization is slow since it writes tons of images to the disk.
## Acknowledgments
This project is based on [CMU's Object Detection and Tracking](https://github.com/JunweiLiang/Object_Detection_Tracking) and the following papers.
If you find this code useful then please cite:
```
@inproceedings{liang2019peeking,
title={Peeking into the future: Predicting future person activities and locations in videos},
author={Liang, Junwei and Jiang, Lu and Niebles, Juan Carlos and Hauptmann, Alexander G and Fei-Fei, Li},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={5725--5734},
year={2019}
}
@inproceedings{liang2020garden,
title={The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction},
author={Junwei Liang and Lu Jiang and Kevin Murphy and Ting Yu and Alexander Hauptmann},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2020}
}
```
## More Examples
