Percipient.ai Inc.

Ivan Kovtun

Vasudev Parameswaran

Jerome Berclaz

Balan Rama Ayyar

Rajendra Jayantilal Shah

Timo Pekka Pylvaenaeinen

Derek Young

Vidyadayini Talapady

Alison Higuera

Simon Chen

Atul Kanaujia

Reechik Chatterjee

Nikhil Kumar Gupta

Anantha Krishnan Bangalore

This description describes a system for identifying individuals within a digital file. The system accesses a digital file describing the movement of unidentified individuals and detects a face for an unidentified individual at a plurality of locations in the video. The system divides the digital file into a set of segments and detects a face of an unidentified individual by applying a detection algorithm to each segment. For each detected face, the system applies a recognition algorithm to extract feature vectors representative of the identity of the detected faces which are stored in computer memory. The system applies a recognition algorithm to query the extracted feature vectors for target individuals by matching unidentified individuals to target individuals, determining a confidence level describing the likelihood that the match is correct, and generating a report to be presented to a user of the system.

Identification of individuals in a digital file using media analysis techniques

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

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Cupertino, CA, United States of America

Ivan Kovtun