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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 17:31, 17 May 2022
, 17 May 2022→Custom Policy
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=== Custom Policy === | === Custom Policy === | ||
Policies represent the business rules that make your application unique. They take the predictions made by the machine learning algorithms and perform informed decisions based on them. The specific implementation of how to make each decision is completely left to the users. The important thing at this point is that you can add or delete the number of policies you want, without affecting the general behavior of the system. | |||
In a parking lot example, you can implement several of them to process the predictions, for instance, to minimize the probability of an erroneous license plate read, you can implement a low pass filter technique that only reports a successful read if it received N matching sequential predictions. Or if a car is detected at the entrance, exit, or any stall, it is only reported once. If a vehicle is moving between stalls, it is flagged as suspicious, etc. | |||
Take, for example, a shoplifting detection. The business rules will receive a raw prediction containing a list of persons and their locations and a predicted behavior for each of them (relaxed, nervous, mad, etc…). It would be insufficient and pretty inaccurate to mark as a shoplifter every person identified as nervous. One can create business rules combining other information to improve the performance of the system, such as the person is classified as nervous and there are no other persons nearby or the person is classified as nervous and is located near items that are popular targets for shoplifters. | |||
As you can see, business rules take predictions as inputs and maintain a running state of the system to perform different tasks accordingly. | |||
=== Custom Action === | === Custom Action === | ||