A Deep Learning Strategy for Abnormal Object Detection by YOLOv8 Model

Machine learning is the ability for computers to learn on their own and improve with time without requiring manual programming. Its main goal is to develop algorithms that can process and analyze massive volumes of data, find patterns in the data, and use the data to make judgments or forecasts. Machine learning algorithms are essentially based on the construction of mathematical models from the input data. These models can recognize correlations between variables since they have been trained on historical data. The model constantly modifies its parameters as new data become available in order to increase prediction or classification efficiency and accuracy.Machine learning in surveillance is akin to equipping a security camera with the ability to think and adapt. It discusses the system to analyze human’s normal and abnormal activities recognition.The proposed research is about the camera system that can do more than just record video; it can also analyze its environment in real time, seeing trends and spotting odd behavior. It can even immediately alert security staff to unusual events, such an unexpected throng or an abandoned object. Machine learning can be used in more complex situations to recognize faces, identify known people from a database, or even forecast potential security events based on behavioral patterns.