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Multi level video AI analytics implementation

By David Yakov, Cawamo CEO

Multi-level video AI analytics can be implemented using various architectural approaches, each offering distinct advantages and considerations. As we know, Cawamo introduced the hybrid architecture to the market as the main relevant architecture for multi-level artificial intelligence, but in this document, we explore the implementation of this technology in three common architectures that Cawamo offers: on-premises, cloud, and hybrid.

Different architectures for multi-level AI video analytics implementation

The most cost-efficient architecture is Hybrid Architecture (Mainly used by the private business sector): Hybrid architectures combine elements of both on-premises and cloud infrastructures, offering organizations the benefits of both approaches. This architecture allows organizations to leverage on-premises resources for sensitive data and real-time processing while utilizing cloud resources for scalability and flexibility.

Pros of Hybrid Architecture:

Real-time Analysis at the Edge: By deploying AI algorithms and video analytics capabilities at the edge, near the video sources or cameras, real-time analysis can be performed locally. This reduces latency and enables immediate response to security events, without relying on cloud connectivity.

Bandwidth Optimization: Edge computing in hybrid architecture reduces the amount of video data that needs to be transmitted to the cloud for processing. Only relevant events, alerts, or summarized data are sent, optimizing bandwidth usage and reducing costs associated with transmitting large volumes of video data.

Offline Operation: Edge devices in hybrid architecture can continue performing video analytics even in the absence of internet connectivity. This ensures uninterrupted surveillance and security monitoring in scenarios where internet connectivity may be intermittent or unavailable.

Cons of Hybrid Architecture:

Cost Considerations: Hybrid architecture may involve additional costs for acquiring edge devices, as well as the ongoing operational costs associated with the cloud processing.

On-Premises Architecture (Mainly used by governmental entities): In an on-premises architecture, the entire video AI analytics infrastructure is deployed within the organization's premises. This approach provides direct control over hardware, software, and data, making it suitable for organizations with stringent security and compliance requirements.

Pros of On-Premise Configuration:

1 Data Privacy and Security: By keeping the video surveillance data within the organization's premises, there is increased control over data privacy and security. This is particularly important when dealing with sensitive security footage, ensuring that the data remains within the organization's boundaries.

2 Reduced Dependency on Internet Connectivity: On-premise configurations do not rely heavily on internet connectivity for processing and analyzing video data. This can be advantageous in scenarios where internet connectivity is unreliable or limited, ensuring continuous security monitoring.

Cons of On-Premise Configuration:

1 Infrastructure Costs: Setting up an on-premise configuration for AI video analytics requires investments in hardware, software, and infrastructure components. These upfront costs can be substantial.

2 Maintenance and Upgrades: all ongoing maintenance, software updates, and hardware upgrades requires a dedicated on-site technician visit. This includes monitoring and managing the AI algorithms, ensuring their accuracy and performance, as well as keeping the underlying infrastructure up to date.

3 Limited Access and Remote Monitoring: On-premise configurations may limit remote access and monitoring capabilities. Remote security personnel or stakeholders might face challenges in accessing the AI video analytics platform from outside the organization's premises.

Cloud Architecture (Mainly used by remote monitoring operators): Cloud-based architectures leverage the computing resources and scalability offered by cloud service providers. This approach provides flexibility, ease of deployment, and access to a wide scalable of cloud-based computing power.

Pros of Cloud-Based Configuration:

Scalability: Cloud-based configurations offer high scalability, allowing organizations to handle large-scale deployments and accommodate increased workloads easily. The cloud infrastructure can scale resources up or down based on demand, providing flexibility and avoiding hardware limitations.

Easy Deployment and Maintenance: Cloud-based solutions eliminate the need for on-premise hardware setup and maintenance.

Cons of Cloud-Based Configuration:

Internet Dependency: Cloud-based configurations fully rely on internet connectivity for data transmission, analysis, and accessing the video analytics platform. A loss of internet connectivity can impact real-time monitoring and response capabilities.

Cost Considerations: Cloud-based solutions typically involve recurring costs based on the subscription or usage model.

A summary:

Although the hybrid architecture (cloud and edge) offers the best flexibility and efficiency for a multi-level AI-based video analytics technique, in cases that require different architectures, on-premise or full cloud, it is also superior in terms of the cost performance level of the system.

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