Uber Technologies, Inc.

Percipient.ai Inc.

Jerome Berclaz

Vasudev Parameswaran

Timo Pekka Pylvaenaeinen

Balan Rama Ayyar

Ivan Kovtun

Rajendra Jayantilal Shah

Thommen Korah

Myra Nam

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

A location detection system performs a process that uses data from a satellite navigation sensor in conjunction with data from a second sensor to determine the accuracy of location estimates provided by the satellite navigation sensor. The system uses the satellite navigation sensor to determine location estimates from at a first time and a second time. The system also uses data from the second sensor to determine a third location estimate, which represents another estimate of the system's location at the second time. The system uses the three location estimates to determine whether the second location estimate satisfies an accuracy condition. If the accuracy condition is satisfied, then the second location estimate may be provided as input to a process.

Location accuracy determination based on data from second sensor

Architecture that summarizes a large amount (e.g., thousands of miles) of street-level image/video data of different perspectives and types (e.g., continuous scan-type data and panorama-type data) into a single view that resembles aerial imagery. Polygons surfaces are generated from the scan patterns and the image data is projected onto the surfaces, and then rendered into the desired orthographic projection. The street-level data is processed using a distributed computing approach across cluster nodes. The collection is processed into image tiles on the separate cluster nodes representing an orthographic map projection that can be viewed at various levels of detail. Map features such as lower-level roads, that are at lower elevations than higher-level roads, and are hidden by higher-level overpassing roads, can be navigated in the map. With the summarized data, the maps can be navigated and zoomed efficiently.

Map-like summary visualization of street-level distance data and panorama data

Growing community of inventors

Sunnyvale, CA, United States of America

Jerome Berclaz