General Electric Company

Lockheed Martin Corporation

Utc Fire & Security Americas Corporation, Inc.

Ge Security, Inc.

Advanced Interfaces, LLC

Nils Oliver Krahnstoever

Peter Henry Tu

Jens Rittscher

Thomas Baby Sebastian

GianFranco Doretto

Xiaoming Liu

Ting Yu

Rajeev Sharma

Jeffrey Michael Ashe

Namsoon Jung

Ser Nam Lim

Kai Erik Thomenius

Kedar Anil Patwardhan

Paulo Ricardo Mendonca

John Charles Hill, Jr

Emilio Schapira

Ambalangoda Gurunnanselage Amitha Perera

Kenneth R Turner

Perera Ambalangoda

Amitha Perera

Aspects of the disclosure provide a method for crowd segmentation that can globally optimize crowd segmentation of an input image based on local information of the input image. The method can include receiving an input image of a site, initializing a plurality of hypothesis based on the input image, dividing the input image into a plurality of patches, calculating an affinity measure of one or more patches to a hypothesis based on a partial response of the patches to a whole body classifier of the hypothesis that includes a combination of weak classifiers, and optimizing assignments of the plurality of patches to the plurality of hypothesis based on the affinity measures of the plurality of patches to the plurality of hypothesis.

Method and system for crowd segmentation

A system for detecting and tracking targets captured in images, such as people and object targets that are captured in video images from a surveillance network. Targets can be detected by an efficient, geometry-driven approach that determines likely target configuration of the foreground imagery based on estimated geometric information of possible targets. The detected targets can be tracked using a centralized tracking system.

Detecting and tracking targets in images based on estimated target geometry

A system, method and program product for camera-based discovery of social networks. The computer implemented method for identifying individuals and associating tracks with individuals in camera-generated images from a face capture camera(s) and a tracking camera(s), wherein the computer implemented method includes: receiving images of an individual from the face capture camera(s) on a computer; receiving images of a track(s) of an individual from the tracking camera(s) on a computer; automatically determining with the computer the track(s) from the images from the tracking camera(s); and associating with the computer the track(s) with the individual(s) and a unique identifier. The present invention has been described in terms of specific embodiment(s), and it is recognized that equivalents, alternatives, and modifications, aside from those expressly stated, are possible and within the scope of the appending claims.

System, method and program product for camera-based discovery of social networks

A device and method for detecting targets of interest in an image, such as people or objects of a certain type. Targets are detected based on an optimized strong classifier descriptor that can be based on a combination of weak classifier descriptors. The weak classifier descriptors can include a user-defined weak classifier descriptor that is defined by a user to represent a shape or appearance attribute that is characteristic of parts of the target of interest. The strong classifier descriptor can be optimized by selecting a subset of weak classifier descriptors that exhibit improved performance in detecting targets in training images.

Device and method for detecting targets in images based on user-defined classifiers

A method and system for distributed tracking of multiple targets is disclosed. Multiple targets to be tracked by a plurality of trackers are detected in a frame. The motion state variable of each of the plurality of trackers is calculated in the E-step of a variational Expectation-Maximization algorithm. Further, the data association variable of each of the plurality of trackers is calculated in the M-step of the algorithm. Depending on the motion state variable and the data association variable, the multiple targets are tracked.

Method and system for distributed multiple target tracking

Embodiments of the invention include a system and a method for determining whether a person is carrying concealed contraband, such as an improvised explosives device or other weapon. The system includes a people tracking video subsystem, a people tracking decisioning subsystem, a concealed contraband detection aiming subsystem, and a concealed contraband detection decisioning subsystem.

Standoff detection systems and methods

A method for re-identifying an image is provided. The method comprises obtaining a plurality of images and generating a correspondence map between the plurality of images. The method further comprises defining a plurality of region signatures for one or more regions comprising the plurality of images and comparing the plurality of images based on the correspondence map and the plurality of region signatures to perform image re-identification.

Method and system for performing image re-identification

A system for detecting and counting individuals in a stationary or moving crowd based on a digital or digitized image captured from a single camera. Initial information is assumed based on a foot-to-head plane homology, where a geometric construct is developed to best enclose image features with a high probability of being an individual within a crowd. These geometric constructs are then subjected to further probabilistic analysis to determine individuals. The vector track of each individual is determined to validate the determination of a group of features as individuals, thereby compensating for occlusion of an individual within any given frame image. A virtual gate is then employed to count the individuals moving past the virtual gate. Also, by weighting portions of the individual, events near the gate can be predicted.

System and method for automatic person counting and detection of specific events

A method for calibrating a projective camera is provided. The method includes acquiring information by detecting at least one object on a substantially flat ground plane within a field of view. A projective camera calibration is performed. A measurement uncertainty is considered to yield a plurality of camera parameters from the projective camera calibration.

Method and system for camera autocalibration

The present invention is a method and apparatus for providing an enhanced automatic drive-thru experience to the customers in a vehicle by allowing use of natural hand gestures to interact with digital content. The invention is named Virtual Touch Ordering System (VTOS). In the VTOS, the virtual touch interaction is defined to be a contact free interaction, in which a user is able to select graphical objects within the digital contents on a display system and is able to control the processes connected to the graphical objects, by natural hand gestures without touching any physical devices, such as a keyboard or a touch screen. Using the virtual touch interaction of the VTOS, the user is able to complete transactions or ordering, without leaving the car and without any physical contact with the display. A plurality of Computer Vision algorithms in the VTOS processes a plurality of input image sequences from the image-capturing system that is pointed at the customers in a vehicle and performs the virtual touch interaction by natural hand gestures. The invention can increase the throughput of drive-thru interaction and reduce the delay in wait time, labor cost, and maintenance cost.

Growing community of inventors

Schenectady, NY, United States of America

Nils Oliver Krahnstoever