Intelligent video surveillance used to be just a demonstration product, meant to showcase capabilities in the security industry. The only mass-produced intelligent video systems were not only limited in quantity but also expensive and constrained by performance, making them largely impractical. However, in recent years, with advancements in hardware processing power, reduced costs, and improved computer technologies, intelligent video surveillance has transitioned from a demo stage to large-scale development. It is now one of the hottest topics in the security sector, with major manufacturers investing heavily in research and development. Industry experts view video intelligence as a revolutionary technology that will shape the future of network monitoring.
Why is intelligent monitoring important? As high-definition network surveillance becomes more common, the issues of high storage demands and bandwidth consumption have become significant challenges. The need for selecting valuable video content has become a key requirement. Imagine storing thousands of images over the years, most of which are useless. Intelligent and selective data acquisition is essential to save bandwidth and storage space. Therefore, the demand for intelligent video surveillance has been growing steadily. Most current monitoring systems, whether analog, hybrid, or fully digital, still operate on the principle of “real-time monitoring and recording,†which is inefficient, costly, and difficult to extract useful information from massive video files. Long-term storage is also a major issue. Moreover, traditional systems rely heavily on human involvement. Intelligent monitoring can reduce labor costs and optimize resource usage, making it an inevitable trend in the evolution of video surveillance.
What are the advantages of intelligent video analysis? In today’s systems, the main challenge is the excessive amount of irrelevant video data being stored and transmitted, which consumes storage space and increases bandwidth usage. Intelligent analysis aims to minimize storage needs and ease bandwidth pressure. For less important videos, low-bitrate streaming can be used, making system queries and investigations more efficient and increasing the overall value of the monitoring system.
Currently, intelligent monitoring technologies mainly include object recognition, trajectory tracking, and environmental compensation. Recognition technologies like face detection, license plate reading, vehicle classification, and traffic light identification are widely used. These systems require accuracy above 98% to meet customer needs effectively. Trajectory tracking includes virtual boundaries, crowd counting, movement tracking, and anomaly detection. Environmental compensation helps maintain clarity in poor weather, low-light conditions, or camera malfunctions, ensuring reliable monitoring even in challenging settings. These features are highly adaptable and suitable for most monitoring applications.
How is intelligent video surveillance implemented? There are two main approaches: front-end and back-end analysis. Front-end analysis uses the camera’s built-in chip to process video data, while back-end analysis relies on software running on a central computer. Both methods use core algorithms to analyze video signals, but the computing platforms differ. Front-end embedded systems run algorithms directly on the camera’s DSP chip, whereas back-end solutions use general-purpose computers.
Front-end vs. Back-end: While most current systems use back-end analysis due to its flexibility and ability to manage storage, this approach requires high bandwidth, which remains a challenge. Front-end analysis reduces transmission load but faces limitations in processing power, especially with complex video tasks. Many DSPs lack sufficient memory and speed, leading to lower accuracy and higher false alarms. However, as embedded systems become more powerful and video analysis algorithms improve, integrating video analytics into front-end devices like cameras and video servers is becoming a growing trend. Front-end analysis offers better real-time performance, transmitting only relevant alarm data and images, significantly reducing data volume and background storage requirements.
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