Sarnoff Corporation

Ford Motor Company Limited

Sri International

Ford Global Technolgoies, LLC

David James Hirvonen

Theodore Armand Camus

John Benjamin Southall

Peng Chang

Ian Gregory Hill

Robert Mandelbaum

Billie Joe Call

André Rieder

Keith James Hanna

Wenyi Zhao

Rakesh Kumar

Supun Samarasekera

Stephen Charles Hsu

Richard Patrick Wildes

Barbara Levienaise-Obadia

Bodgan Matei

William Brian Lehman

Thomas Edward Klinedinst

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 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.

Method and apparatus for refining target position and size estimates using image and depth data

A method and apparatus for classifying an object in an image is disclosed. A depth image is provided. At least one area of the depth image unsatisfactory for object identification are identified. A plurality of two-dimensional projections of surface normals in the depth image is determined without considering the unsatisfactory at least one area. One or more objects are classified based on the plurality of two-dimensional projections of surface normals.

Method and apparatus for classifying an object

A vehicle vision system that identifies pedestrians located proximate a vehicle. The system comprises a sensor array that produces imagery that is processed to generate disparity images. A depth map can be produced from the disparity images. Either the depth map or the disparity images are processed and compared to pedestrian templates. A pedestrian list is produced from the results of that comparison. The pedestrian list is processed to eliminate false pedestrians as determined by inverse eccentricity. The pedestrians are then tracked, and if a collision is possible, that information is provided to the driver and/or to an automated collision avoidance or damage or injury mitigation system.

Method and apparatus for pedestrian detection

A system and method for accurately mapping between image coordinates and geo-coordinates, called geo-spatial registration. The system utilizes the imagery and terrain information contained in the geo-spatial database to precisely align geodetically calibrated reference imagery with an input image, e.g., dynamically generated video images, and thus achieve a high accuracy identification of locations within the scene. When a sensor, such as a video camera, images a scene contained in the geo-spatial database, the system recalls a reference image pertaining to the imaged scene. This reference image is aligned very accurately with the sensor's images using a parametric transformation. Thereafter, other information that is associated with the reference image can easily be overlaid upon or otherwise associated with the sensor imagery.

Method and apparatus for performing geo-spatial registration of imagery

Growing community of inventors

Princeton, NJ, United States of America

David James Hirvonen