Abstract:
A method and apparatus for generating stereo imagery scenario data to be used to test stereo vision methods such as detection, tracking, classification, steering, collision detection and avoidance methods is provided.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
A method and apparatus for generating stereo imagery scenario data (104) to be used to test stereo vision methods (106) such as detection, tracking, classification, steering, collision detection and avoidance methods is provided.
Abstract:
A method and apparatus for classifying an object in an image is disclosed. A plurality of sub-classifiers is provided (410). An object is classified using input from each of the plurality of sub-classifiers (415).
Abstract:
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.
Abstract:
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.
Abstract:
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. An edge score is determined from the edge analysis. The object is classified based on the edge score.
Abstract:
A method and apparatus for classifying an object in an image is disclosed. A depth image is provided. Areas of the depth image unsatisfactory for object identification are eliminated from consideration. A plurality of two-dimensional projections of surface normals in the depth image is determined. One or more objects are classified based on the plurality of surface normals.