Ramakant O Nevatia

8577154, Nov 5, 2013

Jun 11, 2009

12/483050

Ramakant Nevatia - Pacific Palisades CA, US
Fengjun Lv - San Jose CA, US

University of Southern California - Los Angeles CA

G06K 9/00
G06K 9/62
G06K 9/36
G06T 13/00
H04N 7/18
H04N 5/225

382209, 382103, 382107, 382154, 382236, 345473, 348169, 348155

An automated human action recognition system may automatically recognize one or more actions of a human from 2D input image data representing a sequential series of input images of the human performing the one or more actions. Each input image may be from an unknown viewpoint. A computer memory system may contain 2D reference image data representing a plurality of reference actions which a human may perform. The 2D reference image data may include a plurality of linked sequences of key poses, including a linked sequence of key poses for each reference action. For each reference action, each key pose within the linked sequence of key poses for the reference action may consist essentially of 2D image data that is representative of a human figure performing the reference action at a selected point during the reference action. The timing of the selected points within the linked sequence of key poses for the reference action may be based on changes in the position of the human figure during the performance of the reference action. The linked sequence of key poses for the reference action may uniquely distinguish it from the linked sequence of key poses for all of the other reference actions.

20080123968, May 29, 2008

Sep 25, 2007

11/860743

Ramakant Nevatia - Pacific Palisades CA, US
Bo Wu - Los Angeles CA, US

G06K 9/62

382228

A human tracking system for tracking a plurality of humans in motion, in a video of the humans in motion, includes a human detection subsystem, and a combined tracker. The human detection subsystem is configured to generate a detection output by detecting the plurality of humans in a part-based representation, in each one of a sequence of static frames in the video. The human detection subsystem is further configured to account for partial occlusion of one or more of the humans in the image. The combined tracker is configured to receive and combine the detection responses generated by the human detection subsystem, and to track the humans in response to the received detection responses and image appearance properties.

20110085702, Apr 14, 2011

Oct 8, 2010

12/901316

Ramakant Nevatia - Pacific Palisades CA, US
Chang Huang - Los Angeles CA, US
Bo Wu - Mountain View CA, US

UNIVERSITY OF SOUTHERN CALIFORNIA - Los Angeles CA

G06K 9/00

382103

Systems, methods, and computer readable storage media are described that can provide a multi-level hierarchical framework to progressively associate detection responses, in which different methods and models are adopted to improve tracking robustness. A modified transition matrix for the Hungarian algorithm can be used to solve the association problem that considers not only initialization, termination and transition of tracklets but also false alarm hypotheses. A Bayesian inference approach can be used to automatically estimate a scene structure model as the high-level knowledge for the long-range trajectory association.