Princeton Video Image, Inc.

Sri International

Visteon Global Technolgoies, Inc.

Yi Tan

Roy Jonathan Rosser

Subhodev Das

Feng Han

Rakesh Kumar

Ming-Yee Chiu

Jayan Eledath

Howard J Kennedy, Jr

Faroog Abdel-kareem Ibrahim

James L Jeffers

Darrell S DiCicco

Ximin Gong

Peter Von Kaenel

Andrew Coppock

A method for determining whether a target vehicle in front of a host vehicle intends to change lanes using radar data and image data is disclosed, comprising the steps of processing the image data to detect the boundaries of the lane of the host vehicle; estimating a ground plane by determining a projected vanishing point of the detected lane boundaries; using a camera projection matrix to map the target vehicle from the radar data to image coordinates; and determining lane change intentions of the target vehicle based on a moving trajectory and an appearance change of the target vehicle. Determining lane change intentions based on a moving trajectory of the target vehicle is based on vehicle motion trajectory relative to the center of the lane such that the relative distance of the target vehicle from the center of the lane follows a predetermined trend. Determining lane change intentions based on an appearance change of the target vehicle is based on a template that tracks changes to the appearance of the rear part of the target vehicle due to rotation.

Radar guided vision system for vehicle validation and vehicle motion characterization

A system and method for detecting a target in imagery is disclosed. At least one image region exhibiting changes in at least intensity is detected from among at least a pair of aligned images. A distribution of changes in at least intensity inside the at least one image region is determined using an unsupervised learning method. The distribution of changes in at least intensity is used to identify pixels experiencing changes of interest. At least one target from the identified pixels is identified using a supervised learning method. The distribution of changes in at least intensity is a joint hue and intensity histogram when the pair of images pertain to color imagery. The distribution of changes in at least intensity is an intensity histogram when the pair of images pertain to grey-level imagery.

Method and apparatus for detecting targets through temporal scene changes

Image insertion in video streams using a combination of physical sensors

A method for tracking motion from field to field in a sequence of related video broadcast images. The method uses template correlation to follow a set of predetermined landmarks within a scene in order to provide position information of objects in the current image. The current image object position information is compared to position information of the same objects within a reference array data table. The comparison is accomplished through the use of warp equations that map points in the current image to points in the reference array. Motion is tracked according to a velocity prediction scheme utilizing a weighted formula that emphasizes the weight of landmarks that are closer to their predicted position.

Method of tracking scene motion for live video insertion systems

A system for inserting images into live video fields includes a method for rapidly and efficiently identifying landmarks and objects. Initially a first template, having a first pattern similar to one of the distinctive features of the object, is passed over the video field and compared to it in order to preliminarily identify at least one possible distinctive feature as a candidate. A second template is then created by taking one of the major elements of the distinctive feature candidate and extending that element all the way across the second template and then comparing it to the distinctive feature candidate. This eliminates one or more possible falsely identified features. A third template is then created having a pattern formed from another major element of said distinctive feature and extending it all the way across the third template. The third template is then likewise passed over the distinctive feature candidate and compared therewith in order to eliminate still further falsely identified features. The method is continued until all possible false alarm candidates have been eliminated. The process is then repeated in order to preliminarily identify two or three landmarks of the target object. The locations of those objects are then compared to a geometric model to further verify if the object has been correctly identified. The methodology can be tested against a video taped program to determine if it accurately identifies objects.

Pattern recognition system employing unlike templates to detect objects

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Plainsboro, NJ, United States of America

Yi Tan