- 🏷️ Added Label for Every Track
- ⚡ Runs on both CPU & GPU
- 🎥 Supports Video, Webcam, External Camera, and IP Stream
- 🛠️ Development of a Streamlit Dashboard for Object Tracking
Ready-to-Use Google Colab 🔗 Launch Colab
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Clone the repository:
git clone https://github.com/RizwanMunawar/yolov7-object-tracking.git
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Navigate to the cloned folder:
cd yolov7-object-tracking
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Create a virtual environment (Recommended to avoid conflicts):
conda create -n yolov7objtracking python=3.10 conda activate yolov7objtracking
python3 -m venv yolov7objtracking source yolov7objtracking/bin/activate
python3 -m venv yolov7objtracking cd yolov7objtracking/Scripts activate
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Update pip and install dependencies:
pip install --upgrade pip pip install -r requirements.txt
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Run the script:
Select the appropriate command based on your requirements. Pretrained yolov7 weights will be downloaded automatically if needed.
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Detection only:
python detect.py --weights yolov7.pt --source "your video.mp4"
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Object tracking:
python detect_and_track.py --weights yolov7.pt --source "your video.mp4"
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Webcam:
python detect_and_track.py --weights yolov7.pt --source 0
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External Camera:
python detect_and_track.py --weights yolov7.pt --source 1
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IP Camera Stream:
python detect_and_track.py --source "your IP Camera Stream URL" --device 0
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Specific class tracking (e.g., person):
python detect_and_track.py --weights yolov7.pt --source "your video.mp4" --classes 0
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Colored tracks:
python detect_and_track.py --weights yolov7.pt --source "your video.mp4" --colored-trk
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Save track centroids, IDs, and bounding box coordinates:
python detect_and_track.py --weights yolov7.pt --source "your video.mp4" --save-txt --save-bbox-dim
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Output files will be saved in
working-dir/runs/detect/obj-tracking
with the original filename.
YOLOv7 Detection Only | YOLOv7 Object Tracking with ID | YOLOv7 Object Tracking with ID and Label |
Some of my articles/research papers | computer vision awesome resources for learning | How do I appear to the world? 🚀
Ultralytics YOLO11: Object Detection and Instance Segmentation🤯
Parking Management using Ultralytics YOLO11
My 🖐️Computer Vision Hobby Projects that Yielded Earnings
Best Resources to Learn Computer Vision
Roadmap for Computer Vision Engineer
How did I spend 2022 in the Computer Vision Field
Domain Feature Mapping with YOLOv7 for Automated Edge-Based Pallet Racking Inspections
Exudate Regeneration for Automated Exudate Detection in Retinal Fundus Images
Feature Mapping for Rice Leaf Defect Detection Based on a Custom Convolutional Architecture
Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey
Explainable AI in Drug Sensitivity Prediction on Cancer Cell Lines