Guided post-deployment training of customized recognition models
AssigneeWyze Labs, Inc.
InventorsZhongwei Cheng, Vijaya Naga Jyoth Sumanth Chennupati, Lin Chen, Tianqiang Liu
Life of this patent
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Abstract
Introduced here is a training system that allows users to define what they would like their surveillance systems to detect, recognize, or otherwise identify. For simplicity, the use case provided by a given user for teaching purposes may be referred to as a “skill.” A surveillance system can learn each of the skills provided by its respective user from a limited number of examples. For example, the given user may only provide several examples—or even a single example—in contrast to conventional learning approaches.
Claims20
A method comprising:
- storing a set of embeddings in a database, wherein each embedding in the set of embeddings is generated as part of a training operation in which a user defines multiple categories, each of which is associated with at least one embedding included in the set of embeddings;
- appending, to the set of embeddings, an identifier associated with an edge device of a surveillance system, wherein each embedding in the set of embeddings is generated based on a particular sample captured by the edge device;
- acquiring a new sample generated by the edge device, wherein the new sample is associated with the identifier;
- providing the new sample to a model as input, so as to produce an embedding;
- in response to determining, using the identifier, that the new sample and the set of embeddings are associated with the edge device, comparing the embedding against the set of embeddings;
- and
- outputting a prediction based on an outcome of said comparing.
One or more non-transitory, computer-readable storage media comprising instructions recorded thereon, wherein the instructions, when executed by at least one data processor of a system, cause the system to:
- store a set of embeddings in a database, wherein each embedding in the set of embeddings is generated as part of a training operation in which a user defines multiple categories, each of which is associated with at least one embedding included in the set of embeddings;
- append, to the set of embeddings, an identifier associated with an edge device of a surveillance system, wherein each embedding in the set of embeddings is generated based on a particular sample captured by the edge device;
- acquire a new sample generated by the edge device, wherein the new sample is associated with the identifier;
- provide the new sample to a model as input, so as to produce an embedding;
- in response to determining, using the identifier, that the new sample and the set of embeddings are associated with the edge device, compare the embedding against the set of embeddings;
- and
- output a prediction based on an outcome of said comparing.
Dependencies are inferred from the claim text.