  Surveillance systems have emerged as crucial elements in upholding peace and
security in the modern world. Their ubiquity aids in monitoring suspicious
activities effectively. However, in densely populated environments, continuous
active monitoring becomes impractical, necessitating the development of
intelligent surveillance systems. AI integration in the surveillance domain was
a big revolution, however, speed issues have prevented its widespread
implementation in the field. It has been observed that quantum artificial
intelligence has led to a great breakthrough. Quantum artificial
intelligence-based surveillance systems have shown to be more accurate as well
as capable of performing well in real-time scenarios, which had never been seen
before. In this research, a RentinaNet model is integrated with Quantum CNN and
termed as Quantum-RetinaNet. By harnessing the Quantum capabilities of QCNN,
Quantum-RetinaNet strikes a balance between accuracy and speed. This innovative
integration positions it as a game-changer, addressing the challenges of active
monitoring in densely populated scenarios. As demand for efficient surveillance
solutions continues to grow, Quantum-RetinaNet offers a compelling alternative
to existing CNN models, upholding accuracy standards without sacrificing
real-time performance. The unique attributes of Quantum-RetinaNet have
far-reaching implications for the future of intelligent surveillance. With its
enhanced processing speed, it is poised to revolutionize the field, catering to
the pressing need for rapid yet precise monitoring. As Quantum-RetinaNet
becomes the new standard, it ensures public safety and security while pushing
the boundaries of AI in surveillance.
