新技术背景下智能视频分析技术的发展与应用

Core Viewpoint - The evolution of security systems in the financial industry has transitioned from traditional methods to advanced intelligent video surveillance technologies, driven by developments in artificial intelligence and big data [3][4][19]. Group 1: Historical Development of Security Systems - The security systems in the financial sector date back hundreds of years, evolving with technological advancements and changing security needs [3]. - In the early 20th century, banks began implementing intrusion alarms and video monitoring systems to protect cash and valuables [3]. - By the early 21st century, video surveillance technology matured, becoming a crucial component of security systems, primarily for crime deterrence and evidence collection [3][4]. Group 2: Technological Advancements - The rapid development of computer vision technology in the early 2000s introduced capabilities such as target recognition and behavior analysis, enhancing video surveillance systems [4]. - The integration of artificial intelligence and machine learning has led to more accurate and efficient functionalities in video monitoring, including facial recognition and behavior analysis [4][5]. - Intelligent Video Analytics (IVA) automates the analysis of video data, allowing for proactive alerts and enhanced security measures [5][6]. Group 3: Key Technologies in Intelligent Video Surveillance - Video image processing technologies have evolved from traditional methods to advanced algorithms, enabling real-time monitoring and anomaly detection [6][7]. - Facial recognition technology has significantly advanced, utilizing deep learning to improve accuracy and expand application scenarios [8][9]. - Video structural analysis allows for the organization and retrieval of video content, transforming unstructured data into structured information for better analysis [10][11]. Group 4: Data Analysis and Integration - Multi-dimensional data analysis models combine video data with other business data, facilitating comprehensive assessments and predictive modeling in security applications [12]. - The classification of intelligent video analysis technologies can be based on algorithm types, including video improvement, analysis, recognition, and retrieval [13][14]. - The physical location of intelligent video products can be categorized into edge intelligence and cloud intelligence, each serving different analytical needs [17][18]. Group 5: Future Directions and Industry Implications - The financial industry must prioritize top-level design and planning for intelligent video systems, focusing on a combination of edge and cloud intelligence [19][23]. - The emergence of large-scale AI models is expected to drive significant advancements in video processing and understanding, although challenges remain in achieving high accuracy for security applications [24][25]. - A strategic approach to technology adoption is essential, balancing innovation with the potential risks associated with new technologies [25].