Template:DeepStream Reference Designs/Main contents: Difference between revisions

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RidgeRun knows how important documentation is for your project and how a single source of information may decrease the time-to-market of your product.  
== Introduction ==
That's why we've decided to share our expertise with the developer community,
 
creating a centralized wiki with technical documentation, tutorials, examples, and much more!
RidgeRun's DeepStream Reference Designs is a project that provides a robust and modular design, based on the [https://developer.nvidia.com/deepstream-sdk NVIDIA DeepStream SDK], where the building blocks may be replaced to fit a wide variety of use cases. The main objective is to provide an infrastructure for an application using video analytics to perform informed decisions within the application domain. The system could be divided into the following parts:
 
=== Framework ===
 
The framework is the main infrastructure responsible for driving the application state and logic. All modules here do not need modification in order to implement a new application, consequently, this part is the common source that is shared with other applications. There are four principal sections that compose the framework:
 
* '''Camera Capture''': In charge of keeping the control of media sources (cameras, RTSP streams).
* '''AI Manager''': This module will process DeepStream inference defined by the application and will communicate with the next section.
* '''Action Dispatcher''': Uses the data of DeepStream inference to do established actions depending on policies. The actions and policies are defined by the application.
* '''Config Parser''': This module is in charge of loading the configuration files set up to be used by the application.
 
=== Application ===
 
We focus on bringing a solution that the users only need to think about what are they looking for and not develop everything right from the start. So in this part, the users can define the camera source, DeepStream model, and policies that do they serve as a filter of DeepStream inference data that allow for implementation decisions and be executed by the actions, which are also user-defined. All the logic and mechanism to execute all that the user uses in the application is provided by the framework.


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