For a long time, intelligent video surveillance was just a demonstration product used to showcase capabilities in the security industry. The only mass-produced intelligent video surveillance systems were not only limited in quantity but also costly and constrained by performance, making them largely impractical. However, in recent years, with the advancement of hardware processing power and falling prices, along with the progress in computer vision technologies, intelligent video surveillance has transitioned from a demo stage to large-scale development. It has now become a major focus within the security sector. Major manufacturers have invested heavily in research and development, and the industry sees 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 widespread, the challenges of high storage and bandwidth usage have become significant drawbacks. The need for selective and valuable data collection has become essential. Imagine having years of still images that hold no real value. Intelligent and selective data capture is key to saving bandwidth and storage space. Therefore, the demand for intelligent video surveillance has grown over time. Today’s monitoring systems—whether analog, hybrid, or fully digital—still operate on the principle of "real-time monitoring and recording," which not only consumes human resources but also leads to massive video archives that are hard to manage. Long-term storage is also a big issue. Moreover, traditional systems rely heavily on human involvement. Intelligent monitoring can reduce this dependency, saving both time and resources. As a result, intelligent video surveillance is not just a trend—it's the future of video monitoring systems.
What are the advantages of intelligent video analysis?
In current surveillance systems, storage and transmission issues remain the main challenges. A lot of irrelevant video data is stored and sent, leading to wasted space and increased bandwidth usage. The goal of intelligent analysis is to minimize storage requirements and reduce the burden on bandwidth. For less important videos, low-bitrate streaming can be used, making it easier to search and retrieve information and improving the overall value of the system.
Currently, intelligent monitoring technologies 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. The most critical requirement for these systems is an accuracy rate above 98%, ensuring they meet the needs of most users. Trajectory tracking includes virtual lines, area alerts, crowd counting, and motion detection, while environmental compensation deals with poor weather conditions, low lighting, camera movement, and occlusions. These technologies allow for more reliable monitoring even in challenging environments.
How is intelligent video surveillance implemented?
There are two main approaches to implementing intelligent video surveillance: front-end and back-end analysis. Front-end analysis uses the camera’s built-in chip for processing, while back-end analysis relies on software running on a computer. Both methods involve analyzing video through core algorithms, but the difference lies in the computing platform. Front-end systems perform computations on the camera’s DSP chip, while back-end systems use general-purpose computers.
Front-End vs. Back-End: Which is Better?
Currently, most applications use back-end analysis because it’s easier to troubleshoot and helps manage storage pressure. However, this approach requires transmitting large amounts of video data, which puts a heavy demand on network bandwidth—a problem that remains unsolved. On the other hand, front-end analysis reduces the need for data transmission, but the processing power of embedded chips is still limited. Many video analysis tasks require significant computational resources, and most DSPs lack the memory and speed to handle them effectively. This can lead to lower accuracy and higher false alarms.
However, as embedded systems become more powerful and video analysis algorithms improve, it's becoming increasingly common to embed analysis modules directly into front-end devices like video servers and IP cameras. The advantage of front-end processing is its high real-time performance, as only relevant alarm data and images are sent to the backend, significantly reducing data volume and storage load. This makes it a promising direction for the future of intelligent video surveillance.
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