6 steps to integrate Azena-enabled cameras
This article provides system integrators with a simple guide they can use themselves or share with their customers to ensure a full understanding of the scope and flow of their smart video surveillance project.
Azena offers a super fast and easy way to empower surveillance cameras with AI: Just choose the cameras by the manufacturer you prefer, powered by our operating system, and add AI video analytics from our Application Store to enable the devices to perform entirely new tasks, and make your customers' businesses not only safer but more efficient and profitable. The range of AI video analytics apps available for Azena-powered cameras is enormous - each tested and added with just a few clicks.
With the number of options, the temptation rises to equip the video cameras with anything that might be suitable for making operations not only safer but also more efficient. However, just because Azena-powered cameras can be gradually extended with new features, we recommend focusing on a few value-added applications at the very beginning. This is how to enter the world of AI video analytics in a safe and controlled way.
Typical project flow for integrating Azena-enabled cameras
Cameras running Azena's OS make planning and implementation easier and more flexible than ever because AI video analytics apps can be tested, added to and, if necessary, removed at any time with minimal effort and risk. The following project flow is not much different from other IT projects you implement for your customers.
1. Discuss the desired use cases and specific business requirements with your customer.
Strive for a reasonable prioritization of features to start with the ones that add maximum business value with little effort. Less important features from your shopping list can be added later in further iterations. Some questions to ask:
- Which processes could smart video technology make more efficient?
- Which changes create maximum business benefit with little effort?
- In which IT environment will the new camera system be operated?
- Will the Azena-enabled cameras operate on a local network or online?
- With which systems will the new camera system communicate
2. Choose the right hardware from the range of cameras supported by Azena.
Browse cameras from various manufacturers, Ability, Bosch, BSTsecurity, Hanwha Techwin, Topview, and VIVOTEK, that already run the Azena operating system. Here you will find an overview of cameras of various types for various applications.
3. Install your smart cameras and configure the apps for the chosen use cases,
either for individual devices performing specific tasks or as a bulk configuration for a set of devices all performing the same or similar tasks. We assist you with selecting and combining the appropriate video analytics apps from our Application Store.
Also, take a look at our Application Store Guide for System Integrators.
4. Discuss and develop IoT integration logic, customization, and change requirements
related to connecting Azena-enabled cameras to existing systems, such as the automatic barriers in a parking garage controlled by a smart camera system with AI video analytics for license plate recognition, just to give an example.
5. Deploy pilot system with few devices
and pre-installed functions to test and gain experience under live conditions with little effort and risk. Using the Azena Device Management Portal, all installed cameras can be remotely or locally managed in one place. Once the pilot setup has been tested, the rollout can begin.
Azena Device Management Portal - all installed cameras at a glance
6. Make adjustments and deploy the production system.
The main advantage of Azena-enabled cameras is that the configuration of single devices or entire systems can be further adjusted and expanded as needed, even after deployment. Azena's camera OS allows you to flexibly combine and test apps for various applications.
Good to know: Many apps you find in our Application Store are ready to use, i.e. they only need to be installed and immediately do what they are supposed to do. These are usually apps that perform a simple, clearly defined function. Example: Detect the pin entry panel on a POS system, and blur the video recording from that area to maintain privacy. This feature is available and can be installed and run with a few clicks.
Other AI video analytics apps need to be trained to perform more complex tasks. An app that is supposed to recognize and differentiate between objects must first learn to understand and assign optical characteristics. The underlying Machine Learning model is trained for the prototype and continues to learn in live operation. Depending on how complex and specific the AI requirements are, training must be scheduled in the project.
Since the range of apps available in our Application Store is constantly growing, we recommend checking first whether a ready-made camera app is available for the desired application, instead of training or adapting an app with avoidable effort.
For more information take a look at our Application Store Guide for System Integrators and subscribe for updates.
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