CrowdNET - A Case Study Towards Designing Context- driven Computer Vision Techniques to Identify Anomalous Crowd Behavior in Hajj Pilgrimage
We have designed two computer- vision algorithms to detect physical barriers in any image/ video frame, and to count the number of people therein. By employing two algorithms, we overcome data sparsity challenges in number of images of people climbing on gates to train
- Python 3.6.12
git clone https://github.com/FarhatBuet14/CrowdNET.git
cd CrowdNET
pip install -r requirements.txt
- Download the model file from Google Drive. Put the model file on
Barrier_Detection_Localization/output
folder. - Put the test images on the
Barrier_Detection_Localization/test_images
folder
cd Barrier_Detection_Localization/codes
python3 test_image.py
- You will get the result images on the
Barrier_Detection_Localization/output/test_images_results
folder.
- Download the model file from Google Drive. Put the model file on
Crowd_Estimation/model
folder. - Put the test images on the
Crowd_Estimation/test_images
folder
cd Crowd_Estimation/codes
python3 test.py
- You will get the result images on the
Crowd_Estimation/output
folder.