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[NetEase Smart News, October 25] London police recently tested a new facial recognition system, but it led to an embarrassing mistake. During the Notting Hill Carnival, when officers used this technology to identify suspects, there were about 35 false matches, and one person was wrongly arrested.
Although camera-based visual surveillance systems are designed to enhance public safety, they still struggle with real-world conditions. For instance, during the 2011 London riots, facial recognition software only managed to identify and arrest one out of 4,962 suspects.
This failure highlights that current visual surveillance still heavily depends on human operators monitoring hours of footage in dark rooms, which isn't enough to protect people in a modern city. However, recent research suggests that video analytics could be significantly improved through advances in a completely different field: DNA sequence analysis.
By drawing parallels between genetic evolution and video data, these tools can be adapted for automatic visual surveillance. Since the first CCTV cameras were installed in London in 1960, over 6 million have been deployed across the UK. Now, police officers wear body cameras that capture more video and extract complex data for analysis.
Despite this progress, automated visual monitoring remains limited to controlled environments. It can detect property breaches, count people passing through doors, or read license plates accurately. But identifying individuals in crowded public spaces remains unreliable due to the unpredictable nature of outdoor scenes.
To improve video analytics, we need software that can adapt to changing environments rather than giving up on development because of challenges. This represents a fundamental shift in approach. One promising area for handling large, variable datasets is genomics.
Since the first human genome was sequenced in 2001, the amount of genomic data has grown exponentially. The vast volume and constant changes in this data require significant resources to process. Today, scientists can easily access genomic analysis tools to study everything from disease prevention to personalized medicine and human history.
Genomic analysis involves tracking genetic mutations over time, a process similar to how visual surveillance tracks movement. By analyzing differences between video frames, techniques used in genomics can be applied to video data.
Early tests of this "video omics" concept have shown great promise. My team at Kingston University found that video can be analyzed even when shot with a moving camera. We identified camera movement as sudden changes and transformed the footage into something resembling a fixed camera view.
Meanwhile, researchers at the University of Verona have demonstrated that image processing tasks can be coded using standard genomic tools, making software development faster and more cost-effective.
Combining these technologies may eventually lead to the long-promised video surveillance revolution. If we adopt the "video genomics" approach, the next decade could bring smarter cameras. In that case, we should be ready to appear more often in surveillance footage.
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