Template:DeepStream Reference Designs/Main contents

Welcome to RidgeRun's guide to DeepStream Reference Designs	DeepStream Reference Designs Getting Started Environment Setup Evaluating the Project Purchasing a License Project Architecture High Level Design Reference Designs Automatic Parking Lot Vehicle Registration Customizing the Project Implementing Custom Inference Parser Implementing Custom Inference Listener Implementing Custom Policies Implementing Custom Actions Implementing Custom Media Implementing a Custom Application Contact Us
DeepStream Reference Designs
'RidgeRun knows how important documentation is for your project, especially with DeepStream Reference Designs. Regardless of the complexity of the technology, proper documentation can reduce the learning curve and, more importantly, the time-to-market of your product. This wiki is a user guide for our DeepStream Reference Designs project. Introduction RidgeRun's DeepStream Reference Designs is a project that provides a robust and modular design, based on the 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. 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.
RidgeRun support
RidgeRun is an official NVIDIA Partner and we have created this extensive set of documentation to support our joint customers. If you have any questions on the content, please contact us through our contact us page. RidgeRun provides support for embedded Linux development for NVIDIA's platforms, specializing in the use of hardware accelerators in multimedia applications. RidgeRun's products take full advantage of the accelerators that NVIDIA exposes to perform transformations on the video streams achieving great performance on complex processes. This page contains detailed guides and information on how to get started with the DeepStream Reference Designs and start using its full capabilities. To get up-to-speed with your DeepStream Reference Designs, start by clicking below: Error creating thumbnail: Unable to save thumbnail to destination
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