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The Intelligent Surveillance System is a concept that is used to provide support and alert measures to individuals in their homes and businesses. Closed Circuit Television (CCTV) surveillance is employed in business sectors such as jewelry stores and banks all of the time; when theft occurs, the CCTV recordings will be used only for future investigations and will not alert anybody about the theft. When a theft occurs in specific locations such as malls, jewelry stores, vacations, and so on, the suggested work can notify people via SMS and Gmail. The Raspberry Pi will be connected to a standard CCTV camera that will be installed at the selected location. As a result, if a person is discovered at a specific location at a specific time, the user receives an alert message. The real-time object recognition system with the Linux operating system is used to recognize items with a two-dimensional input (Video), where it will detect and show real-time objects based on the environment and surroundings. Mobile Nets creates a real-time project to detect the object for which a frame was created and the object's accuracy was shown. The frame shifts in response to the actions of the objects. It has proven a more accurate real-time alerting tool for the user. Our findings show that in the actual world, the proposed method may be used to build a tracking system with intelligent security and minimal cost. [ABSTRACT FROM AUTHOR]

A surveillance system has become a vital need for house occupants, buildings, and other critical places in today's world, because of the Internet of Things (IoT) influence and broad use. Video surveillance has proved its importance and benefits as an integral component of many home security and safety goals. Cameras are used in various surveillance systems to monitor the surroundings. Someone must check the output in a certain area without taking a break. It is a challenging task for personnel who must secure remote regions such as farms, homes, roadways, and limited places that cannot be observed continuously. Even when no one is there, a location may be safeguarded using a Raspberry Pi and deep learning with a pre-trained model. It monitors the area continuously, identifying any unwanted intruders and sending an alarm message to the appropriate device as soon as feasible. A large number of sample photos, such as people, dogs, cats, and other animals, are input into the system. Using mobile-net single-shot detection, the system compares the unwanted object to the sample photographs, determining the accuracy of common features. As a result, it aids in detecting unwanted presence with more precision than previous methods. We will look at and discuss some of the linked studies in this review study. The aim of this study is to determine how the current automated surveillance system has improved technologically and the hardware used by them and to explore and determine which technology and hardware are deemed more accurate and efficient. [ABSTRACT FROM AUTHOR]