The project aimed to develop a person tracking system using OpenCV and a Raspberry Pi 4. The system was designed to analyze the input from a webcam and determine if a person was entering or leaving a building. The entire code was implemented in Python, and the project duration spanned two months.

During the development phase, the individual undertaking the project took the initiative to learn Python programming and acquired the necessary skills to create the program. Additionally, they designed a custom case to house the Raspberry Pi, ensuring a neat and organized setup.

To power the Raspberry Pi, a Power over Ethernet (PoE) adapter was utilized, allowing for a single cable to provide both power and network connectivity. This setup eliminated the need for an additional power source and simplified the system's deployment.

The system's primary function was to detect and track people entering or leaving the building using the webcam input. OpenCV, a popular computer vision library, provided the necessary tools for implementing the person tracking algorithms. By utilizing techniques such as motion detection and object tracking, the system could accurately determine the direction of movement.

To maintain a comprehensive record of the access events, the system reported the entering and leaving events to a MySQL database. This database not only stored the information about whether a person entered or left the building but also recorded the corresponding date and time of the event. This data logging capability enabled administrators to have a historical perspective on the building's access patterns.

To enhance the accessibility and usability of the collected data, the project integrated SpringShare's LibAnalytics product. LibAnalytics provided a user-friendly interface that allowed staff members to conveniently access and manipulate the data. The integration with LibAnalytics ensured that the collected information could be effectively utilized for monitoring, analysis, and decision-making processes.

Overall, this project successfully utilized OpenCV and a Raspberry Pi 4 to develop a webcam-based person tracking system for building access monitoring. Through self-learning and dedication, the individual behind the project acquired Python programming skills, designed a suitable case for the Raspberry Pi, and leveraged a PoE adapter for power efficiency. The system's recorded data was further integrated into SpringShare's LibAnalytics product to provide staff members with an improved user interface for accessing and analyzing the data.