公开(公告)号：US20190073520A1

公开(公告)日：2019-03-07

申请号：US16120128

申请日：2018-08-31

Applicant: Percipient.ai Inc.

Inventor： Balan Rama Ayyar ,  Anantha Krishnan Bangalore ,  Jerome Francois Berclaz ,  Reechik Chatterjee ,  Nikhil Kumar Gupta ,  Ivan Kovtun ,  Vasudev Parameswaran ,  Timo Pekka Pylvaenaeinen ,  Rajendra Jayantilal Shah

IPC: G06K9/00 ,  G06K9/42 ,  G06F17/30

Abstract: 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.

公开(公告)号：US11636312B2

公开(公告)日：2023-04-25

申请号：US17938042

申请日：2022-10-04

Applicant: PERCIPIENT.AI INC.

Inventor： Vasudev Parameswaran ,  Atul Kanaujia ,  Simon Chen ,  Jerome Berclaz ,  Ivan Kovtun ,  Alison Higuera ,  Vidyadayini Talapady ,  Derek Young ,  Balan Ayyar ,  Rajendra Shah ,  Timo Pylvanainen

IPC: G06K9/00 ,  G06N3/045 ,  G06V10/82 ,  G06V10/774 ,  G06V10/72

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.