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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 18:04, 17 May 2022
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The inference logic is also encapsulated in a separate, independent module. In a parking lot system, you could use a cascade of three different networks: A car detector, a License plate detector, and 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. Having the inference logic in an independent module allows you to highly customize your deep learning pipeline without modifying the rest of the architecture. | The inference logic is also encapsulated in a separate, independent module. In a parking lot system, you could use a cascade of three different networks: A car detector, a License plate detector, and 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. 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 === | ||