Emerging trends are shaping how vast amounts of surveillance data are managed, including innovations like edge computing, cloud integration, AI-enabled analytics and more.
In recent years, the video surveillance ecosystem has undergone intense transformation, driven by a convergence of technological advancements and evolving security needs. These changes have had a significant impact on video surveillance storage solutions, prompting a shift toward more sophisticated and efficient approaches to handling vast amounts of video data.
Each type of video surveillance storage solution (See Various Flavors of Video Storage Alternatives below) has its own advantages and considerations. The choice depends on factors such as the scale of the surveillance deployment, data retention requirements, performance needs, budget constraints and the desired level of accessibility. Ahead, we consult with members of the vendor community to delve into these topics, including emerging storage trends, the influence of artificial intelligence (AI), best practices for security integrators and more.
Direct-to-cloud storage is going to become more common as bandwidth becomes more available and prices — specifically with regards to upstream bandwidth — come down, explains Chris Garner, senior software product manager, Salient Systems, Austin, Texas. Still, he says, on-premise storage solutions will still be the norm for large (200+ cameras) video deployments.
“Also, the resolution war among camera manufacturers shows no sign of abating as ever-higher resolution cameras are introduced and lower resolution cameras become scarce,” Garner adds. “Resolution has a direct impact on storage requirements and increasingly 4MP, 6MP and 4K cameras are becoming common, replacing SD, 720p and 1080p cameras.”
Integrators should be proficient with using storage calculators and should consult with the end user to determine what type of recording is expected or required, advises Garner of Salient Systems.
Choices in resolution, frame rate, lighting conditions and traffic patterns have immense impacts on storage utilization, Garner says. For example, a camera looking down a seldom used, interior hallway is going to produce much less data than an exterior camera looking at a busy entrance.
“Additionally, to make better use of available storage, integrators should consider leveraging multi-streaming configurations to reduce the recording bitrate during times of no activity and higher bitrate when events occur,” he adds.
AI and machine learning analytics are increasing storage requirements due to the fact that users must now not only store the video data, Garner explains, but also the metadata generated by AI and machine learning-powered cameras or by server-based AI analytic applications.
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