A Survey on W-Band Systems for Suspicious Object Detection Using Artificial Intelligence

Our eyes, as well as all of our understanding of the abstract ideas and one-of-a-kind experiences gleaned from the various contacts we have had with the outside world, are all part of what we call “human vision”. Up until recently, computers' capacity for independent thought was extremely limited. Computer vision is a relatively new field of technology, with the aim of enabling computers to recognize and process objects similarly to people. An object tracking program uses deep learning to follow a set of initial object detection and create a unique identifier for each one to track the recognized objects as they move between frames in a video. Numerous experimenters have looked at computer vision, which has evolved from raw data recording to ways and ideas that combine digital image processing, pattern recognition, machine literacy, and artificial intelligence. This paper provides a survey on technologies for design and functionality of AI-based W-band suspicious object identification systems for Internet of Things (IoT) models that track moving humans. The system is made to detect suspicious objects on moving persons using artificial intelligence and wireless connection technology. This paper talks about system design concerns, including hardware and software elements as well as performance criteria for assessing system performance.