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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]