Pioneering Advanced Face Security System Using AI- Deep Learning Techniques
Abstract
Computer vision is an important part of computer science that helps make machines smart and intelligent by detecting and analysing digital images and movies. One major use of this is activity recognition, which automatically sorts the actions an agent is doing. The goal of suspicious activity recognition is to determine what someone is doing by analysing a sequence of observations, taking into account various challenging situations. When the motion detection and tracking features of the surveillance system are activated, the user or owner receives an email. One of the most challenging tasks in computer vision and artificial intelligence research is identifying YOLO3 and CNN objects in both structured and unstructured settings. Video surveillance is very important in today's environment. When artificial intelligence, machine learning, and deep learning were added to the system, the technologies became too advanced. Distinct systems are set up using the combinations above, which help distinguish between distinct suspicious behaviours via live tracking of footage. Human behaviour is the most unpredictable, and it's hard to tell if it's normal or suspicious. In an academic setting, deep learning is used to find normal or suspicious activities. If it detects something suspicious, it sends an alarm message to the relevant person. Monitoring is generally done by taking consecutive frames from the video and using them. There are two key components to the overall structure. The first section calculates the features from the video frames, and the second part uses those features to classify the class as suspicious or normal.
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