Microsoft Technology Licensing, LLC

Agilence, Inc.

Ryan Crabb

Cyrus Soli Bamji

Abbas Rafii

Wei Hua

Xiangrong Chen

Jonathan Cook

Juwei Lu

The present invention is directed to a computer automated method of selectively identifying a user specified behavior of a crowd. The method comprises receiving video data but can also include audio data and sensor data. The video data contains images a crowd. The video data is processed to extract hierarchical human and crowd features. The detected crowd features are processed to detect a selectable crowd behavior. The selected crowd behavior detected is specified by a configurable behavior rule. Human detection is provided by a hybrid human detector algorithm which can include Adaboost or convolutional neural network. Crowd features are detected using textual analysis techniques. The configurable crowd behavior for detection can be defined by crowd behavioral language.

Method of and system for hierarchical human/crowd behavior detection

RGB-Z imaging systems acquire RGB data typically with a high X-Y resolution RGB pixel array, and acquire Z-depth data with an array of physically larger Z pixels having additive signal properties. In each acquired frame, RGB pixels are mapped to a corresponding Z pixel. Z image resolution is enhanced by identifying Z discontinuities and identifying corresponding RGB pixels where the Z discontinuities occur. Thus segmented data enables RGB background substitution, which preferably blends foreground pixel color and substitute background color. The segmented data also enables up-sampling in which a higher XY resolution Z image with accurate Z values is obtained. Up-sampling uses an equation set enabling assignment of accurate Z values to RGB pixels. Fixed acquisition frame rates are enabled by carefully culling bad Z data. Segmenting and up-sampling enhanced video effects and enable low cost, low Z resolution arrays to function comparably to higher quality, higher resolution Z arrays.

Video manipulation of red, green, blue, distance (RGB-Z) data including segmentation, up-sampling, and background substitution techniques

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Los Gatos, CA, United States of America

Ryan Crabb