Artificial intelligence (AI) is revolutionizing how the increasing surveillance data is stored and managed.
Artificial intelligence (AI) is revolutionizing how the increasing surveillance data is stored and managed. The days of simply recording and reviewing footage are gone, replaced by sophisticated systems that analyze, prioritize, and intelligently store information.
This shift has brought a host of new challenges and opportunities for physical security integrators. From balancing the demands of high-resolution video with limited storage capacity to leveraging AI to extract meaningful insights from raw data, the landscape is evolving rapidly.
This article explores how cutting-edge storage solutions are adapting to the AI era. We dive into hybrid systems that blend the immediacy of local storage with the scalability of the cloud, advanced compression techniques that maximize efficiency, and scalable network video recorders (NVRs) designed to meet the demands of tomorrow's surveillance networks. Whether you're an integrator navigating this new terrain or a security professional looking to upgrade your system, this article offers a glimpse into the future of video surveillance storage.
Edge computing: bringing processing closer to the source
A significant advancement in video surveillance is the integration of edge computing, which processes data directly at the camera level — the 'edge' of the network. This significantly reduces the need for data transmission and storage in central systems. By handling data locally, edge computing enables real-time analysis such as motion detection, facial recognition, and object tracking, conserving bandwidth and enhancing the speed and accuracy of security responses.
“One key trend is the adoption of edge computing, where data processing occurs on or near the camera itself (the 'edge' of the network),” said Steve Prodger, CRO of Arcules. “This approach reduces the volume of data that must be sent to the cloud by processing data locally for immediate functions like motion detection, facial recognition, and object tracking. Only relevant information is sent, reducing bandwidth and storage requirements.”
Smart video analytics: selective recording and data prioritization
AI is at the forefront of refining video analytics. Surveillance systems equipped with AI can intelligently categorize and store footage based on relevance, prioritizing critical events over routine video feeds. This capability ensures that only necessary data consumes valuable storage space, which streamlines retrieval and reduces overhead associated with massive data volumes.
“Smart video analytics are crucial, as these AI-powered technologies can identify and categorize footage based on relevance or predefined criteria,” Prodger explained. “Systems can then prioritize and store only valuable data, such as footage containing specific events or anomalies while disregarding redundant or irrelevant video. This targeted data collection optimizes storage use and enhances the efficiency of data retrieval and analysis.”
Jamie Barnfield, Senior Sales Director at IDIS Europe, highlighted the role of deep learning algorithms, stating, “This enables more effective monitoring, quicker response times, and better decision-making. At the same time, storage solutions need to accommodate the increased processing power required for these advanced analytics. The good news is that AI-powered algorithms can intelligently analyze video feeds and compress the data without compromising video quality. This results in reduced storage requirements and lower costs, as well as faster retrieval times for video analysis.”
Hybrid storage solutions: combining the best of both worlds
The emergence of hybrid storage solutions represents another leap forward. These systems combine the immediacy and reliability of local storage with the scalability of cloud solutions. Immediate data needs are met onsite, while less critical data is shifted to the cloud, allowing for efficient long-term storage management.
“Hybrid solutions that combine local and cloud storage are another option,” noted Prodger. “Local storage is used for immediate, short-term video capture and AI processing, while long-term storage is managed in the cloud. This approach leverages both the cloud's scalability and accessibility and the speed and reliability of local processing and storage.”
Advanced compression techniques: efficient use of bandwidth and storage
With the advent of high-resolution video feeds, efficient data management has become crucial. Advanced compression standards like H.265 and H.266 are increasingly vital, reducing file sizes without degrading video quality.
“Developers are also implementing advanced data compression techniques to manage the storage demands of high-resolution video feeds,” Prodger added. “Technologies like H.265 and the emerging H.266 provide efficient video compression, reducing file sizes without compromising video quality, minimizing the storage footprint and bandwidth needed to transmit video data.”
Scalability and flexibility with network video recorders (NVRs)
NVRs are evolving to accommodate the demands of modern surveillance systems. They offer scalable solutions that can manage anywhere from a few to hundreds of channels. This flexibility allows users to expand their systems as needed without significant upfront costs. Users can also enhance their systems by adding more storage or channels, adapting to the growing security needs of businesses.
“NVRs are easily scalable with models designed to handle four to 265 channels, so users get a wide choice of options depending on their application, retention periods, and video analysis requirements,” Barnfield explained. “Most tend to opt for more channels than they need to give them the option to add more cameras or add an affordable AI box in the short-to-medium term future.”
Local AI deployment: enhancing responsiveness and reducing load
AI technologies are increasingly being embedded directly into cameras, particularly useful in areas requiring immediate response or sensitive to frequent false alarms. By processing video data on-site, these cameras can rapidly identify and react to potential threats without the latency associated with centralized data processing.
“We’re also seeing AI increasingly deployed on board cameras, to process video data locally, particularly in small to medium applications, or in larger systems that are plagued by false alarms from specific cameras or that need to cover high-risk areas with AI-powered analytics that alert rapidly and accurately to threats,” Barnfield said. “This reduces the amount of data that needs to be transmitted and stored centrally, saving bandwidth and storage resources while facilitating faster alerts and response times to threats and incidents.”
Looking ahead: the future of video surveillance
The continuous integration of AI into video surveillance is paving the way for transformative advancements. As AI technology advances, surveillance systems are becoming more sophisticated, promising not only enhanced security but also greater operational efficiency and richer insights for businesses.
This evolution is ushering in an era of smarter, more adaptive security solutions that are set to redefine industry standards and provide even more effective tools for protecting assets and ensuring public safety. The future of video surveillance is on the brink of a revolution, promising innovations that will significantly enhance capabilities and drive the industry forward.