This project utilizes computer vision and YOLO-based object detection to count people in a specified zone within a railway station. The system processes a pre-recorded video and identifies the number of individuals in real-time as they move within a defined area. It can easily be adapted for live-stream applications for real-time crowd monitoring.
1112.mp4
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Zone-based Detection:
- A custom polygon zone is defined within the railway station to track individuals.
- The zone acts as a boundary within which people are detected and counted.
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Object Detection:
- The YOLO model detects individuals (people) in the video.
- Each person detected within the defined zone is counted as they move through it.
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Real-Time Processing:
- While this demo uses a pre-recorded video, the system can be extended to live-stream video feeds for real-time processing and crowd monitoring.
- Safety: Helps prevent overcrowding on railway platforms and ensures the safety of passengers.
- Operational Efficiency: Provides valuable insights into crowd patterns, assisting with platform management during peak times.
- Scalability: Can easily be integrated into live-streaming video systems, making it ideal for real-time monitoring in busy transportation hubs or public events.
Just go through the notebook to complete this project.
- Integration with live video streams for real-time crowd monitoring.
- Implementation of more advanced tracking and prediction models.
- Extend functionality to other transportation hubs, such as airports or bus stations.