Sighthound, Inc.

University of Central Florida Research Foundation Inc

Enrique G Ortiz

Syed Zain Masood

Stephen Neish

Guang Shu

Mubarak Shah

Brent Richardson

Ryan Case

Alan Wright

A data-enhanced video viewing system scans videos in order to detect and extract certain objects, such as human faces, so as to compile non-time based synopses, including 'facelines' of people appearing in the video sequence. It can also provide a time-based synopsis that includes timestamps for all detected objects. The data-enhanced video viewing system can be deployed on a network for a client to request data extraction on one or more designated videos. The designated video may be one of those that have been uploaded to social networks, uploaded to online video hosting sites, streamed over the Internet or other network, or uploaded directly by the user.

Data-enhanced video viewing system and methods for computer vision processing

A computer vision pipeline detects tracks and classifies people or other specified class of objects in a steam of video. The ability to not only detect motion, but to distinguish people or other specified objects, can improve the systems usefulness in applications like security monitoring. A motion detection module provides a motion bitmap and a background subtraction module provides a foreground bitmap, and an object tracking module uses these bitmaps identify and track the specified classes of objects. From these objects and tracks, categorized object data can then be generated.

Computer vision pipeline and methods for detection of specified moving objects

The present invention is a complete end-to-end video face recognition system. The invention performs a track-by-track labeling of all tracks within a video. A novel algorithm Mean Sequence SRC (MSSRC) is applied to a face track to perform video face recognition using a joint optimization to leverage all of the available video data and the knowledge that the face track frames belong to the same individual. Additionally the system constructs a probabilistic affinity graph combining appearance and co-occurrence similarities to model the relationship between face tracks in a video. Finally, using this relationship graph, random walk analysis is employed to propagate strong class predictions among similar face tracks, while dampening weak predictions.

Video face recognition

Growing community of inventors

Oviedo, FL, United States of America

Enrique G Ortiz