Sarnoff Corporation

Ford Global Technolgoies, LLC

Ford Motor Company Limited

Sri International

Robert Mandelbaum

Theodore Armand Camus

David James Hirvonen

John Benjamin Southall

Ian Gregory Hill

Billie Joe Call

André Rieder

Harpreet Singh Sawhney

Jayakrishnan Kumar Eledath

Michael Wade Hansen

Lambert Ernest Wixson

Deepam Mishra

Garbis Salgian

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 method for fixating a camera, mounted on a motorized mount, on a target point. The method includes receiving a plurality of images representative of a scene; selecting, within the plurality of images, a first display reference image containing the target point at a first coordinate location; estimating, in a current image within the plurality of images, a transformation that maps the current image to the first display reference image; estimating a second coordinate location of the target point in the current image using the transformation; computing an image slip between the target point in the current image and the target point in the first display reference image; converting the image slip into an angular correction for fixating the camera; modifying the angular correction by using closed-loop control with the motorized mount; and warping the current image using the transformation to align the current image with the first display reference image. This warping step provides a stable view of the scene even in the presence of camera movements that cannot be eliminated using mechanical stabilization, and in the presence of motor control lags and oscillations due to measurement and actuator latencies.

Method and apparatus for fixating a camera on a target point using image alignment

A system that estimates both the ego-motion of a camera through a scene and the structure of the scene by analyzing a batch of images of the scene obtained by the camera employs a correlation-based, iterative, multi-resolution algorithm. The system defines a global ego-motion constraint to refine estimates of inter-frame camera rotation and translation. It also uses local window-based correlation to refine the current estimate of scene structure. The batch of images is divided into a reference image and a group of inspection images. Each inspection image in the batch of images is aligned to the reference image by a warping transformation. The correlation is determined by analyzing respective Gaussian/Laplacian decompositions of the reference image and warped inspection images. The ego-motion constraint includes both rotation and translation parameters. These parameters are determined by globally correlating surfaces in the respective inspection images to the reference image. Scene structure is determined on a pixel-by-pixel basis by correlating multiple pixels in a support region among all of the images. The correlation surfaces are modeled as quadratic or other parametric surfaces to allow easy recognition and rejection of outliers and to simplify computation of incremental refinements for ego-motion and structure. The system can employ information from other sensors to provide an initial estimate of ego-motion and/or scene structure. The system operates using images captured by either single-camera rigs or multiple-camera rigs.

Method and apparatus for estimating scene structure and ego-motion from multiple images of a scene using correlation

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Bala Cynwyd, PA, United States of America

Robert Mandelbaum