Video behavior in intelligent video surveillance system based on deep learning

Due to the limitations of traditional monitoring methods in environmental adaptability, the intelligent video surveillance system has garnered significant attention. The evolution of computer vision technology has facilitated the emergence of this intelligent surveillance system. Specifically, the utilization of deep learning technology in intelligent video surveillance has demonstrated immense potential in analyzing video behaviors and addressing real-world challenges. At present, several deep learning-based video surveillance architectures have been proposed, including hybrid CNN-RNN networks, two-stream networks, attention mechanisms, and transformers. These architectures have found robust applications in various fields, such as action identification, behavior comprehension, and detection of aberrant conduct. In this paper, the author first delves into the differences between traditional video behavior analyses and those based on deep learning. Subsequently, the author elaborates on the applications of the deep learning in video surveillance, highlighting its advantages and capabilities. After that, the author introduces several architectures of intelligent video surveillance based on deep learning, providing insights into their workings and potential impact. Finally, the author puts forward the improvement direction of intelligent video surveillance.