Toyota Jidosha Kabushiki Kaisha

Denso Corporation

Ricoh Company, Ltd.

Denso Corpora Tion

Toshihiko Terada

Hiroaki Ito

Naohide Uchida

Hirotake Ishigami

Kagehiro Nagao

Jun Kishiwada

Masaki Kuwahara

A vicinity supervising device includes an image capturing unit that captures a plurality of images in which an object is simultaneously captured from different locations; a matching processing unit that calculates a parallax point and a reliability point, the parallax point being the number of pixel regions from which a parallax is calculated among a plurality of pixel regions divided by the plurality of images, and the reliability point being the number of pixel regions having high recognition reliability among the plurality of pixel regions; and an evaluation unit that evaluates, based on the parallax point and the reliability point, an imaging environment where the plurality of images are captured.

Vicinity supervising device and method for supervising vicinity of vehicle

In an apparatus for monitoring surroundings of a vehicle based on a plurality of images captured by a plurality of imagers through a windshield of the vehicle, an image acquirer is configured to acquire the plurality of images captured by the plurality of imagers, and a distortion corrector is configured to correct the plurality of images based on an optical distortion distribution of the windshield.

Apparatus for monitoring surroundings of vehicle and method of calibrating the same

In an apparatus for monitoring surroundings of a vehicle carrying the apparatus, a corresponding-point displacement calculator sets a search region for corresponding points in a plurality of images of an object captured simultaneously from different positions and calculate a displacement value between the corresponding points. A search region modifier modifies the search region if a number of corresponding-point displacement values acquired by the corresponding-point displacement calculator within a predetermined period of time is less than a predetermined threshold. An image displacement calculator calculates an image displacement value that is an amount of displacement between the plurality of images based on the corresponding-point displacement values acquired within the predetermined period of time if the number of corresponding-point displacement values acquired by the corresponding-point displacement calculator within the predetermined period of time is equal to or greater than the predetermined threshold. A displacement corrector corrects for an image displacement based on the image displacement value.

Apparatus and method for monitoring surroundings of vehicle

An apparatus for supervising a vicinity of a vehicle includes: an image capturing unit that captures a plurality of images in which an object is simultaneously captured from different locations; a parallax calculation unit that performs a sub pixel estimation based on the plurality of images to calculate a parallax value; a parallax offset calculation unit that calculates a parallax offset value based on the parallax value under a condition where a decimal part S of the parallax value is within a predetermined range; and a parallax correction unit that corrects the parallax value using the parallax offset value.

Vehicle vicinity supervising device and vicinity supervising method

A vicinity supervising device of a vehicle includes an image capturing unit capturing a plurality of images at different locations; a distance acquiring unit acquiring a distance up to the object detected by transmitting and receiving probing waves from/to the object; a first offset calculation unit calculating a first parallax offset value based on the plurality of images and the distance up to the object; and a second offset calculation unit calculating a second parallax offset value based on a change in a parallax in a predetermined period at an identical point among the plurality of images and a travel distance of the vehicle travelling in a predetermined period; and a parallax correction unit correcting the parallax using the first parallax offset value under a condition where a difference between the first parallax offset value and the second parallax offset value is less than or equal to a threshold.

Vicinity supervising device and vicinity supervising method of vehicle

In an object detection device, a distance image generation part generates a distance image based of a pair of brightness images transmitted from a stereo camera. A road surface estimation part estimates a road surface based on the generated distance image. An object detection part groups together positions having a distance value located on/over the estimated road surface and satisfying a predetermined relationship, and detects an object based on the grouped positions. An abnormality detection part detecting occurrence of image abnormality in the brightness image captured during a current detection period. The road surface estimation part estimates the road surface at the current detection period based on the road surface estimated at the previous detection period when the abnormality detection part detects the occurrence of image abnormality in the brightness image captured during the current detection period.

Object detection device

A three dimensional object recognition device has in-vehicle cameras capturing front images of a vehicle, a recognition section and a light shaft judgment section. The recognition section detects a three dimensional object in the front images based on a parallax generated between the front images. The light shaft judgment section detects whether a light shaft phenomenon has happened in a three dimensional object area in at least one of the front images, and judges that the detected three dimensional object is an incorrect three dimensional object caused due to the light shaft phenomenon when detecting the light shaft phenomenon at every time when the recognition section detects the three dimensional object.

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Kariya, Japan

Toshihiko Terada