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Intelligent video analytics has seen significant growth in China, with smart analysis applications beginning around 2005. Initially focused on behavior recognition, the technology has evolved into deep industry-specific applications. As the security sector expanded, domestic video analytics developed rapidly, driven by advancements in image processing, tracking, pattern recognition, and digital signal processing.
Since the 9/11 attacks in 2001, the U.S. significantly increased its investment in video analytics. Research reached a peak between 2002 and 2005. Today, intelligent video analytics is widely applied across multiple domains. Domestically, it is mainly divided into two categories: one uses foreground segmentation and rule-based methods to detect events like intrusion, fighting, or crowd gatherings; the other relies on pattern recognition for object detection, such as vehicle or face recognition.
From an application perspective, there are four main categories: real-time alarms, data statistics, attribute recognition, and image processing. Real-time alarms detect anomalies like intrusions or traffic jams. Data statistics extract information like foot traffic or vehicle counts. Attribute recognition identifies characteristics like gender, age, or license plates. Image processing enhances video clarity through techniques like noise reduction or deblurring.
In terms of product forms, front-end smart cameras and back-end servers are the two main options. Smart cameras offer simplicity and real-time processing, while servers provide greater flexibility and computational power for complex tasks. However, servers may suffer from stability issues, prompting manufacturers to optimize algorithms for embedded devices, especially for outdoor use.
Despite its potential, video analytics still faces challenges. False positives and negatives remain a major concern, and environmental factors like lighting or weather can disrupt performance. Installation and debugging are also complex, requiring specialized knowledge.
Looking ahead, improving accuracy will be the key development direction. Innovations like stereo cameras, self-learning algorithms, and advanced video mining will drive progress. These trends aim to enhance efficiency and reduce human intervention, making intelligent video analytics more reliable and accessible across industries.