Security system with motion detection and occupancy breach detection.

Crowd counting is required in many real-world applications, including Water management is an example of resource management and traffic control, security, and catastrophe management, to name a few. Counting the people in a traditional method, as an example manual counting, keeping track of each individual with registers, and counting with sensors, are time-consuming and exhausting, and may produce erroneous results due to dynamic motions. Crowd-counting technologies based on CCTV video feeds have evolved as a result. However, no device now available can properly distinguish between a human head and other objects, making it impossible to reliably track and count people. As a result, we present a novel system that can track people in transport management system contexts by tracking their heads and counting the number of people entering and exiting the scene. We can use the framework in this project to apply binarization steps to separate the foreground from the background and extract facial features using the Grassmann algorithm. A bounding box can be constructed based on facial traits, and then information about the number of people in the transit system can be provided. Finally, issue an alert to authorities about any person who has exceeded their authority. Then, using a deep learning system, identify the person who will offer attendance. [ABSTRACT FROM AUTHOR]