Sri International

Objectvideo, Inc

Avigilon Fortress Corporation

University of Central Florida Research Foundation, Inc.

Omar Javed

Hui Ting Cheng

Zeeshan Rasheed

Alan J Lipton

Niels Haering

Zhong Zhang

Harpreet Singh Sawhney

Weihong Yin

Ajay Divakaran

Jingen Liu

Peter L Venetianer

Qian Yu

Li Yu

Haiying Liu

Amir Tamrakar

Ishani Chakraborty

Jiejie Zhu

Andrew J Chosak

Gary W Myers

Himaanshu Gupta

Donald Gerard Madden

Jayakrishnan Kumar Eledath

Khurram Hassan-Shafique

Geoffrey R Taylor

Saad Ali

Xiao Chun Cao

Péter L Venetianer

Paul C Brewer

Elizabeth E Shriberg

Mubarak Shah

Andrew J Martone

Steven S Weiner

Mun Wai Lee

Behjat Siddiquie

Wei Hong Yin

Pèter L Venetianer

Asaad Hakeem

Liangyin Yu

Khurram Shafique

David Chisolm

A computing system for recognizing salient events depicted in a video utilizes learning algorithms to detect audio and visual features of the video. The computing system identifies one or more salient events depicted in the video based on the audio and visual features.

Recognizing salient video events through learning-based multimodal analysis of visual features and audio-based analytics

A system for object detection and tracking includes technologies to, among other things, detect and track moving objects, such as pedestrians and/or vehicles, in a real-world environment, handle static and dynamic occlusions, and continue tracking moving objects across the fields of view of multiple different cameras.

Real-time detection, tracking and occlusion reasoning

A complex video event classification, search and retrieval system can generate a semantic representation of a video or of segments within the video, based on one or more complex events that are depicted in the video, without the need for manual tagging. The system can use the semantic representations to, among other things, provide enhanced video search and retrieval capabilities.

Classification, search and retrieval of complex video events

Technology for performing continuous authentication of a mobile device utilizes user activity context data and biometric signature data related to the user. A biometric signature can be selected based on the activity context, and the selected biometric signature can be used to verify the identity of the user.

Continuous authentication of mobile device users

An entity interaction recognition system algorithmically recognizes a variety of different types of entity interactions that may be captured in two-dimensional images. In some embodiments, the system estimates the three-dimensional spatial configuration or arrangement of entities depicted in the image. In some embodiments, the system applies a proxemics-based analysis to determine an interaction type. In some embodiments, the system infers, from a characteristic of an entity detected in an image, an area or entity of interest in the image.

Recognizing entity interactions in visual media

Real-time object detection, tracking and occlusion reasoning

Continuous authentic of mobile device users

A unified framework detects and classifies people interactions in unconstrained user generated images. Previous approaches directly map people/face locations in two-dimensional image space into features for classification. Among other things, the disclosed framework estimates a camera viewpoint and people positions in 3D space and then extracts spatial configuration features from explicit three-dimensional people positions.

3D visual proxemics: recognizing human interactions in 3D from a single image

Classification, search, and retrieval of complex video events

A video camera may overlook a monitored area from any feasible position. An object flow estimation module monitor the moving direction of the objects in the monitored area. It may separate the consistently moving objects from the other objects. A object count estimation module may compute the object density (e.g. crowd). A object density classification module may classify the density into customizable categories.

Object density estimation in video

A method for segmenting video data into foreground and background portions utilizes statistical modeling of the pixels. A statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons. The method for segmenting video data may be further incorporated into a method for implementing an intelligent video surveillance system. The method for segmenting video data may be implemented in hardware.

Video segmentation using statistical pixel modeling

A method for segmenting video data into foreground and background () portions utilizes statistical modeling of the pixels &#x39b; statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared () with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons. The method for segmenting video data may be further incorporated into a method for implementing an intelligent video surveillance system The method for segmenting video data may be implemented in hardware.

A video sequence of a field of view within an environment is received. Targets are detected in the video sequence. Target geo-positional information is received. Correspondences between the targets detected in the video sequence and the target geo-positional information are determined and used to calibrate the camera and to geo-register a field of view of the camera.

Automatic camera calibration and geo-registration using objects that provide positional information

A computer-based method for automatic detection of water regions in a video include the steps of estimating a water map of the video and outputting the water map to an output medium, such as a video analysis system. The method may further include the steps of training a water model from the water map; re-classifying the water map using the water model by detecting water pixels in the video; and refining the water map.

Method for detecting water regions in video

Growing community of inventors

Franklin Park, NJ, United States of America

Omar Javed