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Bosch Security and Safety Systems I UK
Video Analytics

The importance of artificial intelligence in delivering the Power to Predict

The latest advances in artificial intelligence (AI) use machine learning and deep learning. They give cameras the ability to self-learn and enable built-in Video Analytics to be taught to detect customer-specific objects or situations. They can also transform a task from requiring human input to a successfully automated one. And tackle more complex tasks faster, easier, and with greater accuracy. We continue to leverage the power of AI to enable users to understand their environment more deeply, so they can respond proactively. And ultimately, predict unforeseen or future situations. Having the power to predict can prevent things from happening and strengthen the protection of people and property. It can even help to avoid potential damages and uncover business opportunities that create new revenue streams or reduce operational costs.


Human review

Today, many video security systems still require intensive human monitoring or need operators to review video footage of events over an extended time. Advances in technology are focused on delivering the highest image quality like starlight X and HDR X. But this approach is limited and doesn’t help with timely screening when sifting through large amounts of video data or managing hundreds of cameras at once. Industry forecasts indicate that the video surveillance camera market will grow to 44 billion U.S. dollars by 2025. Now, more than ever, there's an urgent need to deliver precise information at the right time. Assisted review solutions can be created by adopting AI. Video security cameras with built-in AI, like Video Analytics from Bosch, can understand what they’re seeing, monitor risks against a threshold, and alert people if there is a real threat, or a situation needs attention, the moment it happens.


Assisted review

Harnessing the Power to Predict means making efficient use of the rich and versatile video data generated by video systems. Assisted review is a first step towards predictive solutions. It combines AI with the Internet of Things (AIoT), and the highest image quality - a trend that we have embraced since it first emerged. Our assisted review solutions help users identify possible undesirable events, or provide all kinds of statistics using AI. By applying Video Analytics, they enable video security cameras to understand what they see. The system alerts users to any threats or situations that need attention the moment they happen, and frees up time and resources.

What we offer for Assisted review


Automated review

More powerful hardware and ever-increasing volumes of video data are the main drivers for deep learning. Using it brings us closer to imitating humans and enables ‘things’, like video security cameras, to better understand their surroundings. Deep learning based video analytics enhances detection capabilities in congested scenes, and ignores potential disturbances such as vehicle headlights or shadows, extreme weather, and sun reflections. It drives higher precision detection capabilities and delivers accuracy levels beyond 95 percent. And it brings users one step closer towards predictive solutions by providing scene understanding at a deeper level with the ability to recognize patterns and improved granularity, diversity, and accuracy of data. Deep learning opens up a new world of possibilities and enables our video solutions to automatically review certain tasks.

What we offer for Automated review



Ultimately, we want our customers to know what’s next. With the power to predict they can. Which means they can strengthen security and safety, avoid potential damage, and gain insights to inform decisions and uncover new opportunities. It’s a journey that’s definitely started, but there are still some miles to travel. It requires continuous investments in, and adoption of, AI to make ‘things’ understand what they’re seeing, so they can add ever-deeper context to captured video. It means supporting informed decisions by consolidating, analyzing, and augmenting data from multiple sensors to recognize patterns and predict future situations. And ensuring solutions are sustainable, support ecological business practices, and the data they generate is highly accurate.

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