Percipient.ai Inc.

Simon Chen

Vasudev Parameswaran

Jerome Berclaz

Balan Rama Ayyar

Ivan Kovtun

Rajendra Jayantilal Shah

Derek Young

Vidyadayini Talapady

Alison Higuera

Atul Kanaujia

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.

Systems and methods for rapid development of object detector models

Growing community of inventors

Pleasanton, CA, United States of America

Simon Chen