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DeepStream Reference Designs/Project Architecture/High Level Design: Difference between revisions
DeepStream Reference Designs/Project Architecture/High Level Design (view source)
Revision as of 21:39, 20 May 2022
, 20 May 2022→Custom Deep Learning Models
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=== Custom Deep Learning Models === | === Custom Deep Learning Models === | ||
It should be mentioned that the inference logic is also encapsulated in a separate, independent module, called engine, with a well-established interface. This module bases its operation on the | It should be mentioned that the inference logic is also encapsulated in a separate, independent module, called engine, with a well-established interface. This module bases its operation on the [https://developer.nvidia.com/deepstream-sdk DeepStream SDK], and allows, within its configurations, to use different inference models according to the application being developed. So, for instance, in a parking lot system, you could use a cascade of three different networks: | ||
* A car detector | |||
* A License plate detector | |||
* An OCR (optical character recognition) system | |||
This configuration will vary from application to application. A shoplifting detection will probably implement a person detector along with a behavior analysis model. A speed limit enforcer will likely use a car detector and a tracker. A neuromarketing-powered billboard will use a face detector and a gaze tracker. As you can see, having the inference logic in an independent module allows you to highly customize your deep learning pipeline without modifying the rest of the architecture. | |||
=== Custom Inference Listener === | === Custom Inference Listener === | ||