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On October 25, NetEase Smart News reported that London police recently tested a new facial recognition system—but it didn’t go as planned. During the Notting Hill Carnival, officers used the tool to identify suspects, but it generated around 35 false matches. One person was even wrongly arrested. This highlights the challenges of deploying such systems in real-world settings.
Although camera-based visual surveillance systems are designed to enhance public safety, they still struggle with real-life complexity. For instance, during the 2011 London riots, facial recognition software only managed to identify one out of nearly 5,000 suspects. This shows how far the technology still has to go before it can be fully trusted.
The limitations of current systems mean that most surveillance still relies on human operators watching hours of footage from dark rooms. That’s not enough to protect people in a fast-moving city. But recent research suggests a promising solution: video analytics could be revolutionized by advances in DNA sequence analysis. By drawing parallels between genetic evolution and video data, scientists are exploring ways to improve automated surveillance.
Since the first CCTV cameras were installed in London in 1960, the country has now deployed over 6 million cameras. Police officers also wear bodycams that capture more detailed video and extract richer data. However, even with this, automated visual monitoring is still limited to controlled environments—like detecting intrusions or reading license plates. Identifying people in crowded public spaces remains unreliable due to unpredictable changes in lighting, angles, and movement.
To truly advance video analytics, we need software that can adapt to changing conditions instead of failing when faced with complexity. This is where genomics comes in. Since the completion of the first human genome in 2001, genomic data has grown exponentially. Handling such massive and dynamic datasets has driven the development of powerful computational tools—tools that could soon be applied to video analysis.
Today, researchers can easily access genomic analysis services to study everything from disease prevention to personalized medicine. The core idea behind genomics is analyzing how genes evolve through mutations. This is similar to how visual surveillance tracks movement and changes over time. By applying genomic techniques to video data, we can detect patterns and track individuals more accurately.
Early experiments with “video omics†have already shown promise. Researchers at Kingston University found that videos taken from moving cameras can be analyzed effectively by identifying sudden movements and treating them as if the camera were fixed. Meanwhile, teams at the University of Verona have demonstrated that image processing tasks can be adapted using standard genomic tools, significantly reducing development costs and time.
Combining these approaches could lead to a breakthrough in video surveillance, bringing us closer to the smart, automated systems once promised. In the coming decade, we may see smarter cameras that can recognize faces, track behavior, and adapt to complex environments. If this trend continues, we’ll all be more visible than ever before. (From: thenextweb | Compilation: NetEase See | Compilation Robot Review: Rain Egg)
Follow the NetEase Smart public account (smartman163) for the latest updates on the AI industry and emerging technologies.
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