583
edits
DeepStream Reference Designs/Project Architecture/High Level Design: Difference between revisions
DeepStream Reference Designs/Project Architecture/High Level Design (view source)
Revision as of 21:49, 20 May 2022
, 20 May 2022→Custom Inference Listener
| Line 69: | Line 69: | ||
The inference listener component is responsible for transmitting the information metadata, which is obtained at the output of the neural networks used in the DeepStream pipeline. If we are in a parking lot application, the inference metadata will contain information about the detected vehicle and its respective license plate. If the application corresponds to a security system in a shopping center, the inference results contain essential data about the people detected around the areas of interest in the shopping center. | The inference listener component is responsible for transmitting the information metadata, which is obtained at the output of the neural networks used in the DeepStream pipeline. If we are in a parking lot application, the inference metadata will contain information about the detected vehicle and its respective license plate. If the application corresponds to a security system in a shopping center, the inference results contain essential data about the people detected around the areas of interest in the shopping center. | ||
Therefore, this component has the task of transmitting the metadata that is being obtained in real-time. To achieve this goal, the user can make use of message brokers, which already have well-defined implementations and interfaces to transmit data through the so-called publisher-subscriber pattern. An example of this type of component could be RabbitMQ, which allows establishing communication through a local server, and | Therefore, this component has the task of transmitting the metadata that is being obtained in real-time. To achieve this goal, the user can make use of message brokers, which already have well-defined implementations and interfaces to transmit data through the so-called publisher-subscriber pattern. An example of this type of component could be RabbitMQ, which allows establishing communication through a local server, and can be integrated with the DeepStream framework using the plugins like the [https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgbroker.html Gst-nvmsgbroker]. However, since DeepStream is based on the GStreamer framework, the user is free to create their custom inference listener element, which can be added to the media pipelines or use another component that is already developed. | ||
=== Custom Inference Parser === | === Custom Inference Parser === | ||