Data collection and user feedback in edge video devices

A system, comprising:

• a plurality of installations, wherein:
• each installation comprises a plurality of video cameras;
• and
• each video camera comprises:
• an artificial intelligence logic configured to:
• identify candidate objects according to a first confidence threshold and a second confidence threshold, wherein a true positive candidate object and a false positive candidate object are differentiated based on a confidence score;
• track a motion of the candidate objects within a visual field, wherein each candidate object comprises a bounding box defining its location in the visual field;
• separate the candidate objects into false positive candidate objects and false negative candidate objects according to their tracked motions, wherein the separating comprises:
• separating a candidate object as a false positive candidate object in response to the bounding box of a candidate object occurring within an area of non-motion designated by the presence of a single logic value associated with non-motion within the bounding box;
• and
• separating a candidate object as a false negative candidate object in response to the bounding box of the candidate object occurring within an area of motion designated by the presence of a single logic value associated with motion within the bounding box, wherein a relative physical location of the area of motion is illustrated relative to the bounding box;
• and
• present at least a portion of the false positive candidate objects and false negative candidate objects for further annotation, wherein the presenting comprises sending a request to annotate a false negative candidate object that has been separated in response to determining that the object's bounding box is present in one or more previous frames in an area of motion.

A method of collecting training examples for updating an object identification and motion tracking model, the method comprising:

• identifying candidate objects according to a first confidence threshold and a second confidence threshold, wherein a true positive candidate object and a false positive candidate object are differentiated based on a confidence score;
• tracking a motion of the candidate objects within a visual field, wherein each candidate object comprises a bounding box defining its location in the visual field;
• separating the candidate objects into false positive candidate objects and false negative candidate objects according to their tracked motions, wherein the separating comprises:
• separating a candidate object as a false positive candidate object in response to the bounding box of a candidate object occurring within an area of non-motion designated by the presence of a single logic value associated with non-motion within the bounding box;
• and
• separating a candidate object as a false negative candidate object in response to the bounding box of the candidate object occurring within an area of motion designated by the presence of a single logic value associated with motion within the bounding box, wherein a relative physical location of the area of motion is illustrated relative to the bounding box;
• and
• presenting at least a portion of the false positive candidate objects and false negative candidate objects for further annotation, wherein the presenting comprises sending a request to annotate a false negative candidate object that has been separated in response to determining that the object's bounding box is present in one or more previous frames in an area of motion.