Data collection and retraining in edge video devices

A system, comprising:

• a computational system configured to:
• evaluate a validation data set with a first production detector;
• use annotation of an output data of the first production detector to generate a surrogate data set;
• and
• train a filter with the surrogate data set;
• and
• a device, comprising:
• an artificial intelligence logic configured to:
• receive the trained filter;
• evaluate an entire field data set with the filter to generate a field data subset;
• evaluate the field data subset with the first production detector to generate a final field data subset;
• add the final field data subset to the validation data set;
• update a training data set with the validation data set;
• train the second production detector with the updated training set;
• and
• replace the first production detector with the second production detector, wherein the field data subset is run through a second production detector to generate a locally stored portion of the final field data set.

A method, comprising:

• evaluating a validation data set with a first production detector;
• annotating output data of the first production detector to generate a surrogate data set;
• training a filter with the surrogate data set;
• evaluating an entire field data set with the filter to generate a field data subset, wherein a portion of the entire field data set comprises surveillance video;
• evaluating the field data subset with the first production detector to generate a final field data set;
• adding the final field data to the validation data set;
• updating a training data set with the validation data set;
• training the second production detector with the updated training set;
• and
• replacing the first production detector with the second production detector, wherein the field data subset is run through a second production detector to generate a locally stored portion of the final field data set.