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Intelligent video analysis has seen rapid development in the domestic market, with smart applications starting around 2005. Initially focused on behavior analysis, it has now evolved into deep applications across various industries. As the security sector expands, video analytics has grown significantly, driven by technological advancements and increasing demand.
Intelligent video analysis (Video Analytics) is a multidisciplinary field that combines image processing, tracking, pattern recognition, software engineering, and digital signal processing. Since the 9/11 attacks in 2001, the U.S. has invested heavily in this technology. From 2002 to 2005, it was a peak period for research and development in this area.
Currently, domestic intelligent analysis technology is mainly divided into two categories: one involves foreground extraction methods to identify targets in video frames and apply rules to detect events such as intrusion, fighting, or crowd gathering. The other uses pattern recognition to model and detect specific objects like vehicles, faces, or license plates.
From an application standpoint, there are four major categories of intelligent analysis technologies: real-time alarms, data statistics, attribute recognition, and image processing. Real-time alarm systems detect events like line crossing, intrusion, or fights and trigger alerts accordingly. Data statistics analyze video content to generate reports, such as traffic flow or foot traffic counts. Attribute recognition identifies features like facial expressions, vehicle logos, or colors, while image processing enhances video quality through techniques like noise reduction or deblurring.
In terms of product forms, front-end smart cameras and back-end server products dominate the market. Smart cameras offer real-time analysis and simpler system architectures, while servers handle complex algorithms with better performance and flexibility. However, servers may face stability issues in embedded environments, prompting manufacturers to optimize algorithms for front-end devices, especially in outdoor settings.
Despite its potential, intelligent video analysis still faces challenges. False positives and negatives remain a concern, and environmental factors like lighting or weather can interfere with accuracy. Installation and debugging also require expert knowledge, making it difficult for non-professionals to manage.
Looking ahead, the main development trend is improving accuracy. Future systems will likely incorporate stereo cameras for depth information, self-learning algorithms to adapt to different environments, and advanced video data mining to handle large datasets efficiently. These innovations aim to enhance reliability, reduce manual effort, and unlock new possibilities in surveillance and beyond.