Securing tomorrow: The evolution of AI and cloud in video surveillance

Artificial intelligence, bolstered by cloud computing — the use of hosted services like data storage, servers, networks and software by way of the internet — is transforming the technology landscape. One sector that these systems are particularly helping to revolutionize is video surveillance. 

Historically, surveillance video has been plagued by inefficiency: in police investigations, for example, poor image quality and coverage gaps have often hindered progress. However, video surveillance usage is still an increasingly integral part of the world today. As ongoing urbanization, population growth, and more complex security threats like cyber-attacks have heightened public safety risks, the demand for more sophisticated surveillance continues to grow. Thanks to continued innovation within the sector, the efficiency of products on the market is improving — more powerful processors are allowing for faster real-time analysis of feeds, while advancements in machine learning algorithms mean aspects like unusual behavior, unattended bags, and even weapons can be detected from large amounts of video data with ease. These technologies are ultimately helping to keep society safer, and not just in terms of criminal risk: AI-backed video surveillance is also helping in areas like retail services, crowd control at events, and traffic monitoring in cities.

The integration of artificial intelligence into the world of video surveillance has propelled the industry to new levels of precision and effectiveness. According to market projections, AI-powered video surveillance could be worth $36.1 billion by 2032, with a compound annual growth rate of 20.9%. This growth, fueled by AI’s ability to manage vast amounts of footage accurately and with less manpower, offers the public sector and security industries a powerful tool for monitoring and analyzing data.

Driven by deep learning algorithms, current surveillance systems can also examine audio and visual data for specific criteria, identify people and objects, scan vehicles and license plates, and monitor events for unusual behavior — all with exceptional accuracy. This kind of instant analysis is of critical importance in high-stakes environments like airports, schools, businesses and homes, where a matter of seconds could be the difference between saving lives.

The true potential of AI-backed video surveillance comes when it is paired with the necessary infrastructure to process, store, and analyze big data in real-time: that’s where cloud computing comes into play.

Cloud computing technology — which allows users to conveniently access, manage, and store data and software via the internet instead of local devices — was first made commercially popular by tech companies as far back as 2006. The most common iteration of cloud technology nowadays can be seen in everyday email systems and smartphone backups, but the cloud is utilized all over. Take central government, for example, where the U.S. Federal Cloud Computing Strategy is being implemented to “drive savings, to improve security, and to deliver mission-serving solutions faster.” 

While many programs employ different types of cloud computing, the outcome is the same — cloud technology frees users from the day-to-day burdens of managing and maintaining large amounts of traditionally-stored data, via expensive (and often bulky) hardware systems. The cloud instead acts as a remote server. Where video surveillance has traditionally required an onsite video recorder (NVR or DVR) to record and store video data, the cloud allows users to handle the data remotely. There are several further benefits of cloud computing: operational efficiency, limitless and scalable cloud storage, enhanced security, and a centralized data system that can be sized up or down to suit the requirements of an organization. 

The cloud also provides AI-driven video surveillance software with the necessary infrastructure, scalability, and processing power to enhance monitoring capabilities. These days, device management for surveillance cameras, such as reviewing stored footage, live feeds, or searching in-depth for specific characteristics within video data can be operated from the convenience of a mobile app on the other side of the world. Businesses and security services are also now able to garner actionable insights to make intelligent decisions about their operations by processing video data in a matter of seconds, rather than reviewing by hand.

A major player within this space is Hanwha Vision, a company that’s currently building out an ecosystem of AI and cloud-based surveillance solutions as a platform-centric, all-in-one solutions provider. Among these is FLEX AI, an software  application that uses machine learning AI to recognize physical objects detected by surveillance systems. This allows for precise, targeted monitoring depending on the user’s needs.

The SightMind tool allows for information to be organized visually by way of widgets, charts, and dashboards, a good example of how surveillance information can be leveraged to improve business practices: through analyzing things like footfall for retailers to determine optimal sales periods, for cities that want to measure road safety in preparation for events, and for factories that are seeking to operate more efficiently.

OnCloud, another cutting-edge solution in the Hanwha Vision ecosystem, offers direct-to cloud management of multiple locations and assets of an organization. Through NLP — natural language processing, a type of artificial intelligence that uses machine learning to enable computers to communicate with and understand human languages — it provides low-latency access to live and recorded video, searchable clips, event notifications, and more. Device Manager Pro (DMPro) uses the cloud to provide device maintenance and system health monitoring capabilities in real-time, delivering updates to users whenever their camera deployments require attention.

This technology ecosystem, driven by AI and cloud computing, can be utilized at all corners of society, ultimately helping to lead us toward a smarter, safer, and more interconnected world.

AI-backed video surveillance is now being implemented everywhere: from commerce to public transit and food manufacturing, with significant experimental surveillance tech in the works.

This technology is no longer a novelty, but a part of our day-to-day lives. We can see its implementation at the earliest stages of urban development, with smart city initiatives — which operate on the best of the best in video surveillance and digital technology — being built around the world. Singapore, coined the “smartest city in the world,” uses an app to notify citizens of emergencies like traffic accidents, which are identified through AI-powered surveillance. 

However, fostering trust in surveillance technology as its capabilities broaden is paramount. For the industry to be ethical, organizations must be transparent and accountable, clearly communicating the scope of surveillance with the public and, where possible, seeking consent. At the backend of this technology, algorithms should also be regularly updated to reduce the risk of bias. This will ensure that the benefits of these innovations are fair, and that their impact is positively received.

The combination of AI and the cloud is facilitating exciting advancements for global security, providing our data-rich world with efficient cloud storage capabilities, and helping businesses to grow by empowering them with a better understanding of their audiences. 

As these technologies continue to evolve, ensuring their responsible deployment is critical to harnessing the full potential of AI cloud computing video surveillance. Migrating to cloud-based tech across all industries — not just video surveillance — is well underway. Despite requiring a changeover in processes, clear advantageous benefits like simplified big data analytics, improved information management, and increased flexibility in an ever-changing world make any migration challenges well worthwhile. By putting this kind of technology in place now, we’re securing a future where safety and efficiency are at the forefront.