AI-based video analytics are becoming more and more popular as we see a rise in emerging technologies in the security landscape. There are many benefits to using AI for video analytics, such as increased accuracy and efficiency. However, there are also some drawbacks to using AI in the cloud. Let’s talk about some of the pros and cons of running AI-based video analytics in the cloud.

Pros:

• Improved accuracy: data can be processed more quickly and accurately using video analytics in the cloud, as opposed to on-premises video analytics solutions.
• Real-time insights: which can be used to improve security, optimize operations, and make better business decisions.
• Reduced costs: eliminates the need for expensive on-premises hardware and software, and can be purchased on a pay-as-you-go basis.
• Scalability: video analytics in the cloud can be scaled up or down to meet changing needs, without incurring additional costs.
• Ease-of-use: gives you centralized remote access to all available footage. 

 Cons:

• Video analytics can require a significant amount of processing power and storage, which can be costly over a period of time.
• Video analytics can generate a large volume of data, which may be difficult to manage and store effectively.
• Video analytics can be complex and time-consuming to set up and configure properly.
• Video analytics in the cloud can propose cyber threats. It is important to note that your cloud system addresses the highest levels of physical security for your datacenters to ensure compliance with regulations.
  
  
  

Video analytics in the cloud provide many benefits over traditional on-premises video analytic solutions. Deploying analytics in the cloud gives companies the ability to add analytics to any camera system making them more flexible, more affordable and more reliable over time. 

However, it may not be the best solution for every business or organization depending on their landscape, but there is always a hybrid approach to take into account. If an analytics program on the edge device detects something, it generates a video clip and then hands it to the cloud. The cloud does another process against it to confirm that the alert is worth attention. This way the analytics are doing their job and the cloud is filled with relevant data and very little noise. 

Also another factor to consider is if the cloud goes down. We never want to put all of our eggs in one basket, but a mixed approach to video analytics (on the edge vs. the cloud) allows us to have video analytics running at the edge with security on the perimeter so we have a safety net of some protection when the cloud is not in order. 

Overall, running AI-based video analytics in the cloud has its advantages, as it is a powerful tool that can offer significant advantages for businesses looking to improve their operational efficiencies, security and gain new insight into their business through custom dashboards. The obstacles may still be far too great from a cost perspective (today) for most applications. But like anything the best bet is to talk to someone and assess the use case and best approach to solving the problem.

If you’re looking to implement video analytics in your business, contact us today. Our team of experts will work with you to find the best solution for your needs. SoloSquid and partners like EAIGLE can assist in helping you successfully Plan, Deploy and Maintain your AI solutions. With a range of enterprise computer vision solutions like, People Counting and Heat Mapping to more complex solutions like, Automated Vehicle Access Control and Public Restroom Monitoring Systems. 

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