Large gatherings often create safety and management challenges for schools, events, and public spaces. Existing crowd counting methods rely on manual observation, which prove slow, inaccurate, and difficult to use in real time. We developed a reliable system that automatically counted people and estimated crowd density using video footage to improve situational awareness and support better decision-making without requiring expensive or complex equipment.
We designed and built an artificial intelligence-based crowd counting system that uses standard cameras. The system analyzes video frames to detect individuals and estimate how many people occupied a given area. We trained and tested the model using sample crowd images and videos to improve accuracy across different lighting conditions and camera angles. The system processes videos in real time and displayed crowd counts and density levels through a simple user interface. Our design emphasizes ease of use, low cost, and compatibility with existing camera systems.
The completed system demonstrates that artificial intelligence can deliver accurate and consistent crowd estimates without human involvement. Compared with manual counting methods, our system reduces errors and produced results more quickly. The project shows that automated crowd monitoring can help prevent overcrowding, improve space management, and enhance public safety. This work provides a practical solution for organizations seeking to better understand and manage crowd conditions in real-world environments.
