Cloud Video Surveillance
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TORONTO - April 7, 2021 - (Newswire.com)
It's a constant dilemma for security integrators on how to invest in their video surveillance systems and whether it makes more sense to invest as a one-time payment or pay less upfront but have recurring monthly VSaaS (Video Surveillance as a Service) payments. Often, the CapEx approach defeats the OpEx approach. Yaro Lisitsyn, co-founder and CEO at VXG discusses if the initial, one-time approach works today. VXG Inc. is a technology company offering end-to-end video management software and video surveillance solutions.
Storage
Video surveillance is all about recording and costs for video storage.
- Storage - How much is enough? Changing storage capacity can cost almost the same as the initial deployment.
- Distribution of storage devices across multiple locations to balance traffic. In most cases, the bandwidth limitations will dictate the architecture.
- With multiple storage devices/systems, maintenance is critical (including regular health checks and possible replacement).
- No storage/system works forever, upgrading hardware is inevitable.
Accessibility
When there are cameras generating live video, data and storage, accessing them is necessary. This is done through VXG web browser and mobile apps by point-to-point (P2P) or cloud connection.
- P2P is a direct connection to a camera or on-premise storage (NVR), which requires either network configuration like port forwarding or a VPN, or the use of P2P protocols like WebRTC. There is a labor cost for port forwarding, and a security concern - users are allowing someone to access their network.
- Cloud connection (cloud cameras) means video is re-transmitted through the cloud. There are extra costs for cloud-based video processing, but it solves the connectivity problem in virtually 100% of cases.
Artificial Intelligence
AI is a trending topic. However, how many success stories are there about video AI products for video surveillance?
There are two main problems - accuracy and cost. There is a large gap between an AI demo and a scalable product, with few successful, complete solutions on the market. Typically, AI runs on the edge or on the cloud.
- Ideally, AI runs on a camera, but cameras with built-in AI and video analytics require significant processing. These cameras need more CPU power and are more expensive, but a bigger problem is the life cycle of an AI camera, typically lasting only 5-10 years.
- Cloud AI processing is relatively expensive, however, there is clear separation between slow and fast-changing parts, like sensors and optics, versus AI processing requirements. The algorithms are advancing so fast that there is continuous deployment/delivery in the cloud.
Market Shift
Cloud-based services are seeing a surge in adoption by users in all industries, including video surveillance. Moving from standard VMS (video management systems) and traditional CCTV, DVR and NVR deployments. When choosing a cloud video surveillance system/architecture and determining how the cloud can best support your business, there are five key factors to consider: bandwidth, storage, cost, security and accessibility.
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Original Source: Does It Make Sense to Move Video Surveillance to the Cloud?
