China's Security Intelligence Status

China's Security Intelligence Status 1. Smart security applause: The ideal fullness, the realistic sensibility Although intelligent video analytics (IVA) is often seen as a "pampered" technology in the market, its implementation in China has been far from smooth. Several global IVA leaders—like ObjectVideo and Vidient from the U.S., NICE, Mate, and IOImage from Europe, and IOmniscient from Australia—entered the Chinese market, initially dominating it with their advanced solutions. However, due to the mismatch between their technologies and China’s unique conditions, these foreign companies struggled to fully penetrate the domestic market. Meanwhile, most local IVA manufacturers have focused on specific use cases, such as target tracking, boundary crossing, counting, and object detection. These are typically applied in controlled environments like prisons, museums, warehouses, and subway stations. However, when it comes to more complex, real-world scenarios—such as public spaces or dynamic surveillance—the performance of these systems drops significantly. Many products rely heavily on alarm accuracy as a key metric, but this often highlights their limitations in practical deployment. Intelligent video analytics is currently stuck in an awkward situation: it's neither fully accepted nor entirely dismissed. To reach large-scale adoption, it must not only leverage its inherent technical strengths but also overcome critical bottlenecks. With the right breakthroughs, the explosion of smart security intelligence may be just around the corner. 2. Technology can hardly meet application requirements While smart video technology has advanced rapidly in recent years, its real-world applications still face significant challenges. The market is flooded with products like network cameras and network matrices, but the system quality remains inconsistent. Issues such as image delay, jitter, and low clarity are common. Moreover, unstable hardware can lead to crashes, restarts, and missed alerts. The algorithms behind intelligent video analysis work well in ideal settings, but in real environments, only basic functions are usually implemented. One major issue is the difficulty in eliminating false positives. For example, background modeling in motion detection helps reduce missed alarms, but it can't completely eliminate false ones. In perimeter protection systems that rely on target recognition, false alarms remain a key performance indicator. The root cause lies in the gap between the background model and actual usage. A more adaptable model reduces false positives, but it requires higher technical capabilities. Factors like camera height, lighting conditions, and environmental changes all affect model accuracy. Currently, no single background model can handle every scenario perfectly. For instance, a 6-meter-high camera captures different images than a 2-meter-high one, and day and night conditions demand different models. Additionally, many systems lack the ability to interpret behavior. While they can detect movement and identify targets, they can’t determine intent—such as whether an intruder is just passing by or planning to enter a restricted area. This limitation hinders the effectiveness of automated security responses. Feature recognition also demands high-quality images. Beyond clear resolution, the target must be clearly visible for accurate identification. However, current AI systems still lag behind human perception. Lighting and angles can drastically alter what the system sees. Meanwhile, CPU processing power remains a bottleneck. Complex algorithms require heavy computation, and current DSP chips often fall short, making it hard to develop advanced embedded systems. This increases both development costs and the resistance to real-world adoption.

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