Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousne ss of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the charact eristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous eve n for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgmen t ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that t he artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..