Sarnoff Corporation

Sri International

Sensar, Inc.

Ford Motor Company Limited

Ford Global Technolgoies, LLC

Autoliv Electronics

Theodore Armand Camus

David James Hirvonen

Peng Chang

John Benjamin Southall

Mayank Bansal

Thomas A Chmielewski, Jr

Aveek Kumar Das

Ian Gregory Hill

Robert Mandelbaum

Billie Joe Call

André Rieder

Keith James Hanna

Jayakrishnan Kumar Eledath

John R Fields

Gary Alan Greene

Richard Patrick Wildes

Kevin Carl Kaighn

Marcus Salganicoff

Ankit Kumar

Aastha Jain

Manish Kumar

Aparna Taneja

The present invention provides an improved system and method for measuring quality of both single and stereo video images. The embodiments of the present invention include frequency content measure for a single image or region-of-interest thereof and disparity measure for stereo images or region-of-interest thereof.

System and method for measuring image quality

The present invention is a system and a method of segmenting and detecting objects which can be approximated by planar or nearly planar surfaces in order to detect one or more objects with threats or potential threats. The method includes capturing imagery of the scene proximate a platform, producing a depth map from the imagery and tessellating the depth map into a number of patches. The method also includes classifying the plurality of patches as threat patches and projecting the threat patches into a pre-generated vertical support histogram to facilitate selection of the projected threat patches having a score value within a sufficiency criterion. The method further includes grouping the selected patches having the score value using a plane fit to obtain a region of interest and processing the region of interest to detect said object.

System and method of detecting objects

The present invention provides a collision avoidance apparatus and method employing stereo vision applications for adaptive vehicular control. The stereo vision applications are comprised of a road detection function and a vehicle detection and tracking function. The road detection function makes use of three-dimensional point data, computed from stereo image data, to locate the road surface ahead of a host vehicle. Information gathered by the road detection function is used to guide the vehicle detection and tracking function, which provides lead motion data to a vehicular control system of the collision avoidance apparatus. Similar to the road detection function, stereo image data is used by the vehicle detection and tracking function to determine the depth of image scene features, thereby providing a robust means for identifying potential lead vehicles in a headway direction of the host vehicle.

Apparatus and method for object detection and tracking and roadway awareness using stereo cameras

A vision system for a vehicle that identifies and classifies objects (targets) located proximate a vehicle. The system comprises a sensor array that produces imagery that is processed to generate depth maps of the scene proximate a vehicle. The depth maps are processed and compared to pre-rendered templates of target objects that could appear proximate the vehicle. A target list is produced by matching the pre-rendered templates to the depth map imagery. The system processes the target list to produce target size and classification estimates. The target is then tracked as it moves near a vehicle and the target position, classification and velocity are determined. This information can be used in a number of ways. For example, the target information may be displayed to the driver, the information may be used for an obstacle avoidance system that adjusts the trajectory or other parameters of the vehicle to safely avoid the obstacle. The orientation and/or configuration of the vehicle may be adapted to mitigate damage resulting from an imminent collision, or the driver may be warned of an impending collision.

Vehicular vision system

A vision system that forms a map of a scene proximate a platform, e.g., a vehicle, that determines the actual ground plane form the map, and that corrects the map for differences between the actual ground plane and an assumed ground plane. The vision system may remove the actual ground plane from the map to prevent false positives. The vision system can further identify and classify objects and, if appropriate, take evasive action.

Method and apparatus for ground detection and removal in vision systems

The present invention provides a system and method for detecting edges of an object in a pre-crash sensing environment. The method includes extracting vertical edges from peaks in an array of column sums of absolute values of normalized horizontal derivates computed in the intensity image. The pixels with corresponding range image depths proximate the estimated object's closest surface contribute the column sums. The search is conducted for all likely combinations of left/right edge pairs preferably within a pre-determined horizontal range about the object's detected center. The left/right edge pairs that delimit a width within the pre-determined range of possible widths for the object are selected. Alternatively, an additional search is performed on each pair of edges to support any angular or inward slant on the edges of the object.

Method and apparatus for detecting edges of an object

A method and apparatus for classifying an object in an image is disclosed. Edges of an object are detected within a region of interest. Edge analysis is performed on a plurality of sub-regions within the region of interest to generate an edge score. The object is classified based on the edge score.

Method and apparatus for differentiating pedestrians, vehicles, and other objects

A method and apparatus for performing collision detection is described. An object is detected within a first operational range of an object tracker. A classification of the object is determined using the object tracker. The object tracker tracks the object. The object is detected within a second operational range of a collision detector. A safety measure is activated based on the classification using the collision detector.

Method and apparatus for object tracking prior to imminent collision detection

A stereo vision based collision avoidance systems having stereo cameras that produce imagery that is processed to produce a depth map of a scene. A potential threat is detected in the depth map. The size, position, and velocity of the detected potential threat are then estimated, and a trajectory analysis of the detected potential threat is determined using the estimated position and the estimated velocity. A collision prediction based on the trajectory analysis is determined, and then a determination is made as to whether a collision is imminent based on the collision prediction and on the estimated size of the potential threat.

Stereo-vision based imminent collision detection

A method of quickly and robustly localizing a pair of concentric circular region boundaries in a digital image, such as the pupil and iris boundaries in an image of a human eye is disclosed. Specularities in the image are optionally filled. An object containing concentric regions, e.g., a pupil and iris, is found within the image.

Method and apparatus for providing a robust object finder

A method and apparatus for classifying an object in an image is disclosed. A plurality of sub-classifiers is provided. An object is classified using input from each of the plurality of sub-classifiers.

Method and apparatus for classifying an object

A method and apparatus for classifying an object in an image is disclosed. Edge energy information and/or intensity variation information is generated for an object in a region of interest of the image. The object occupying the region of interest is classified based on the edge energy information and/or intensity variation information.

A vehicle vision system that uses a depth map, image intensity data, and system calibration parameter to determine a target's dimensions and relative position. Initial target boundary information is projected onto the depth map and onto the image intensity. A visibility analysis determines whether the rear of a target is within the system's field of view. If so, the mapped image boundary is analyzed to determine an upper boundary of the target. Then, vertical image edges of the mapped image boundary are found by searching for a strongest pair of vertical image edges that are located at about the same depth. Then, the bottom of the mapped image boundary is found (or assumed from calibration parameters). Then, the target's position is found by an averaging technique. The height and width of the target are then computed.

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Marlton, NJ, United States of America

Theodore Armand Camus