yolov5-object-tracking

New Features

• YOLOv5 Object Tracking Using Sort Tracker
• Added Object blurring Option
• Added Support of Streamlit Dashboard
• Code can run on Both (CPU & GPU)
• Video/WebCam/External Camera/IP Stream Supported

Coming Soon

• Option to crop and save detected objects
• Dashboard design enhancement

Pre-Requsities

• Python 3.9 (Python 3.7/3.8 can work in some cases)

Steps to run Code

• Clone the repository

git clone https://github.com/RizwanMunawar/yolov5-object-tracking.git

• Goto the cloned folder.

cd yolov5-object-tracking

• Create a virtual envirnoment (Recommended, If you dont want to disturb python packages)

### For Linux Users
python3 -m venv yolov5objtracking
source yolov5objtracking/bin/activate

### For Window Users
python3 -m venv yolov5objtracking
cd yolov5objtracking
cd Scripts
activate
cd ..
cd ..

• Upgrade pip with mentioned command below.

pip install --upgrade pip

• Install requirements with mentioned command below.

pip install -r requirements.txt

• Run the code with mentioned command below.

#for detection only
python ob_detect.py --weights yolov5s.pt --source "your video.mp4"

#for detection of specific class (person)
python ob_detect.py --weights yolov5s.pt --source "your video.mp4" --classes 0

#for object detection + object tracking
python obj_det_and_trk.py --weights yolov5s.pt --source "your video.mp4"

#for object detection + object tracking + object blurring
python obj_det_and_trk.py --weights yolov5s.pt --source "your video.mp4" --blur-obj

#for object detection + object tracking + object blurring + different color for every bounding box
python obj_det_and_trk.py --weights yolov5s.pt --source "your video.mp4" --blur-obj --color-box

#for object detection + object tracking of specific class (person)
python obj_det_and_trk.py --weights yolov5s.pt --source "your video.mp4" --classes 0

• Output file will be created in the working-dir/runs/detect/exp with original filename

Streamlit Dashboard

• If you want to run detection on streamlit app (Dashboard), you can use mentioned command below.

Note: Make sure, to add video in the yolov5-object-tracking folder, that you want to run on streamlit dashboard. Otherwise streamlit server will through an error.

python -m streamlit run app.py

YOLOv5 Object Detection	YOLOv5 Object Tracking	YOLOv5 Object Tracking + Object Blurring	YOLOv5 Streamlit Dashboard

References

• https://github.com/ultralytics/yolov5
• https://github.com/abewley/sort

My Medium Articles

• https://medium.com/augmented-startups/yolov7-training-on-custom-data-b86d23e6623
• https://medium.com/augmented-startups/roadmap-for-computer-vision-engineer-45167b94518c
• https://medium.com/augmented-startups/yolor-or-yolov5-which-one-is-better-2f844d35e1a1
• https://medium.com/augmented-startups/train-yolor-on-custom-data-f129391bd3d6
• https://medium.com/augmented-startups/develop-an-analytics-dashboard-using-streamlit-e6282fa5e0f
• https://medium.com/augmented-startups/jetson-nano-is-rapidly-involving-in-computer-vision-solutions-5f588cb7c0db
• https://chr043416.medium.com/how-can-computer-vision-products-help-in-warehouses-aa1dd95ec79c

For more details, you can reach out to me on Medium or can connect with me on LinkedIn