AI In CCTV Technology – Future Surveillance Cameras
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The UK is often referred to as one of the most surveilled nations on Earth. The total number of CCTV cameras in the UK stands at somewhere between 4 million and 6 million, around 7.5 cameras for every 100 people in the country, according to a report published by the British Security Industry Association (BSIA). This is the third-highest total on the planet behind the US and China. However, the overall picture of the UK’s street surveillance ecosystem is muddled, with some cameras too old to produce quality images, others aimed at entryways rather than streets. Renewed concern about the safety of public streets has prompted the UK government to announce the doubling of a “Safer Streets” fund to £45 million, with planned measures including more CCTV in public places such as parks.
Most CCTV cameras in the UK are actually privately owned – either put up by businesses looking to protect their premises, or attached to private residencies for security. According to some estimates, just 1 in 70 CCTV cameras are state-owned, and many of these are placed in and around public buildings.
According to theconversation.com, this has resulted in a disparate and fragmented CCTV ecosystem, with cameras concentrated in commercial districts rather than in residential neighborhoods. Even in commercial areas, many cameras were initially installed to monitor entryways into buildings – not to enhance street safety.
CCTV technology is evolving. New digital cameras offer significantly improved surveillance capabilities. Sharper recordings now offer clearer pictures that could be used as trustworthy evidence in legal proceedings. And the growing profusion of internet-connected “smart cameras”, offers a new way to analyse footage via Artificial Intelligence, both in real-time or via recordings after incidents have occurred. Such AI, in use across some CCTV ecosystems, can be used to automatically analyze unfolding situations, potentially enhancing public safety. These systems are proving useful for identifying objects on train tracks, monitoring crowd size, recognizing unusual behavior, and identifying known suspects in a dragnet of recordings from a certain area.
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