Object Detection and Tracking

This project uses a pre-trained Mask R-CNN model to detect and segment objects in video frames and then applies OpenCV’s CSRT tracker to follow the largest detected object across the video. The pipeline includes downloading pre-trained models, running detections, visualizing masks and bounding boxes, and exporting a tracked output video.

Tech 🛠️ Languages and Tools :

             

• Python : Popular language for implementing Neural Network
• Jupyter Notebook : Best tool for running python cell by cell
• Google Colab : Best Space for running Jupyter Notebook with hosted server
• OpenCV : Best Library for working with images
• Numpy : Best Library for working with arrays in python
• MatPlotLib : Library for showing the charts in python
• GDown : Download Resources from Google Drive

💻 Run the Notebook on Google Colab

You can easily run this code on google colab by just clicking this badge

🪪 License

This project is licensed under the MIT License.