Data collection and classifier training in edge video devices

Abstract

A digital video camera architecture for updating an object identification and tracking model deployed with the camera is disclosed. The invention comprises optics, a processor, a memory, and an artificial intelligence logic which may further comprise artificial neural networks. The architecture may identify objects according to the confidence threshold of a model. The confidence threshold may be monitored over time, and the model may be updated if the confidence threshold drops below an acceptable level. The data for retraining is ideally generated substantially internal to the camera. A classifier is generated to process the entire field data set stored on the camera to create a field data subset also stored on the camera. The field data subset may be run through the model to generate cases that may be used in further monitoring, training, and updating of the model. Classifiers may also be generated for images in different domains (e.g., lighting, weather, surveillance area, indoor, outdoor, urban, rural, etc.). These classifiers can be used to train the model to accurately identify objects and features independent of the domain of origin of the image being evaluated.