US Patent:
20230023164, Jan 26, 2023
Inventors:
- Santa Clara CA, US
Atul KANAUJIA - San Jose CA, US
Simon CHEN - Pleasanton CA, US
Jerome BERCLAZ - San Jose CA, US
Ivan KOVTUN - San Jose CA, US
Alison HIGUERA - San Josae CA, US
Derek YOUNG - Carbondale CO, US
Balan AYYAR - Oakton VA, US
Rajendra SHAH - Cupertino CA, US
Timo PYLVANAINEN - Menlo Park CA, US
Assignee:
PERCIPIENT.AI INC. - Santa Clara CA
International Classification:
G06N 3/04
G06V 10/72
G06V 10/774
G06V 10/82
Abstract:
A computer vision system configured for detection and recognition of objects in video and still imagery in a live or historical setting uses a teacher-student object detector training approach to yield a merged student model capable of detecting all of the classes of objects any of the teacher models is trained to detect. Further, training is simplified by providing an iterative training process wherein a relatively small number of images is labeled manually as initial training data, after which an iterated model cooperates with a machine-assisted labeling process and an active learning process where detector model accuracy improves with each iteration, yielding improved computational efficiency. Further, synthetic data is generated by which an object of interest can be placed in a variety of setting sufficient to permit training of models. A user interface guides the operator in the construction of a custom model capable of detecting a new object.