Beijing Smarter Eye Technology Co. Ltd.

Nokia Technologies Oy

Other

An Jiang

Feng Cui

Ran Meng

Haitao Zhu

Qiwei Xie

Hua Chai

Zunying Pang

Xi Chen

Jian Li

Yan Wang

Chao Yang

Zhao Sun

Xinliang Wang

Yuan Hao

Provided is an Aobject-based short range measurement method, a short range measurement device, a short range measurement system, and a storage medium. The short range measurement method includes: identifying a target object, and acquiring border information about an ROI of the target object; acquiring two groups of geometric constraint points of the target object with respect to a left-eye camera and a right-eye camera respectively; acquiring pixel coordinates of each geometric constraint point and a border pixel size corresponding to the border information, and calculating a monocular distance estimation value of the target object; acquiring an overall disparity of the two groups of geometric constraint points, and calculating a binocular distance estimation value of the target object in accordance with the overall disparity; and acquiring a final measurement value in accordance with the monocular distance estimation value and the binocular distance estimation value.

Object-based short range measurement method, device and system, and storage medium

An RGB-D fusion information-based obstacle target classification method includes: collecting an original image through a binocular camera within a target range, and acquiring a disparity map of the original image; collecting a color-calibrated RGB image through a reference camera of the binocular camera within the target range; acquiring an obstacle target through disparity clustering in accordance with the disparity map and the color-calibrated RGB image, and acquiring a target disparity map and a target RGB image of the obstacle target; calculating depth information about the obstacle target in accordance with the target disparity map; and acquiring a classification result of the obstacle target through RGB-D channel information fusion in accordance with the depth information and the target RGB image.

RGB-D fusion information-based obstacle target classification method and system, and intelligent terminal

Provided is a binocular camera depth calibration method, a binocular camera depth calibration device, a binocular camera depth calibration system and a storage medium. The method includes: acquiring a plurality of groups of images of a 2D code calibration board at different positions so as to acquire disparity maps of the 2D code calibration board; acquiring a 2D code template image of a target region; matching the 2D code template image, so as to determine and store matching region information; determining a position of the 2D code template image in each disparity map in accordance with the matching region information, and calculating an average disparity value of the 2D code template image at the position; acquiring a plurality of groups of average disparity values, and calculating a final disparity value; and calibrating a depth of a binocular camera.

Binocular camera depth calibration method, device and system, and storage medium

An image edge extraction device for an automated driving system is capable of extracting an image edge in a rapid manner. The device employs an image edge extraction method that includes: acquiring a grayscale image of a target image; calculating an edge magnitude and an edge angle of the grayscale image, so as to generate an edge magnitude image and an edge angle image; detecting an edge peak of the edge magnitude image in accordance with the edge angle image, so as to acquire each edge point of the edge magnitude image and acquire an edge peak detection result; and performing double-thresholding segmentation on the edge peak detection result through a flooding method, so as to acquire an image edge of the target image.

Image edge extraction method and device, and automated driving system

A method for building a camera imaging model includes: converting world coordinate values of a random point P into camera coordinate values of a target camera in accordance with a predetermined mode; converting the camera coordinate values into image coordinate values of the target camera; and converting the image coordinate values into digital image coordinate values and building the camera imaging model. The converting the image coordinate values into the digital image coordinate values includes performing a spatial sampling operation on the random point P, and adjusting coordinate values of an origin to image coordinate values through calculation. The method can be used in an automated driving system for a vehicle.

Method and device for building camera imaging model, and automated driving system for vehicle

The present disclosure provides a detection method and a detection device for a digital camera. The detection method includes: Step 1 of moving the to-be-detected auto-focusing lens assembly to a position spaced apart from a subject by a first distance; Step 2 of performing automatic exposure control until an average luminance value of a central region of an image is in a stable state; Step 3 of locking an automatic exposure control parameter; Step 4 of taking a first image; Step 5 of determining sizes of four corner regions in the first image; Step 6 of counting average luminance values of the four corner regions; and Step 7 of, in the case that the average luminance values of the four corner regions are identical to each other, determining that the to-be-detected auto-focusing lens assembly is installed at an accurate position. According to the present disclosure, it is able to detect where or not the auto-focusing lens assembly is installed at the accurate position in an automatic and efficient manner.

Method and device for detecting digital camera

A lane departure warning method includes steps of: collecting a road image by an imaging device; detecting a lane marking in accordance with the road image, so as to extract a position of the lane marking and an angle of the lane marking relative to a running direction of the vehicle; acquiring steering information and a movement speed of the vehicle by an OBD system; judging whether or not the vehicle is unconsciously running on the lane marking in accordance with the position of the lane marking, the angle of the lane marking relative to the running direction of the vehicle and the steering information of the vehicle; in the case that the vehicle is unconsciously running on the lane marking, recording a duration within which the vehicle is unconsciously running on the lane marking; and judging whether or not to send a lane departure warning to the vehicle.

Method and device of lane departure warning and automobile driving assistance system

The present disclosure provides a disparity map-based obstacle detection method, a disparity map-based obstacle detection device, and a vehicle assistant driving system. The method includes steps of: acquiring a disparity map and a V disparity map in accordance with an image including a road surface; simulating the road surface in accordance with the V disparity map; identifying a first obstacle in accordance with a simulation result; extracting an object whose disparity value is greater than a first threshold in accordance with the disparity map, and subjecting the object to morphological operation so as to identify a second obstacle; extracting an object whose disparity value is smaller than a second threshold in accordance with the disparity map, and subjecting the object to morphological operation so as to identify a third obstacle; and screening the first obstacle, the second obstacle and the third obstacle in accordance with a training model, so as to detect the obstacle, the training model being acquired through machine learning in accordance with correct obstacle information and erroneous obstacle information. According to the present disclosure, it is able to improve the robustness.

Detecting method and device of obstacles based on disparity map and automobile driving assistance system

An obstacle warning method includes the steps of: acquiring scenario images at a current sampling time and a previous sampling time, and a map about first relative distances between respective viewing points in a viewing field and a vehicle; acquiring a profile and marking information of an obstacle and a map about a second relative distance between the obstacle and the vehicle in accordance with the map about the first relative distances; calculating a map about a third relative distance between the obstacle and the vehicle at a previous sampling time in accordance with the map about the first relative distances at the previous sampling time, the profile and the marking information of the obstacle at the current sampling time and a motion vector of the obstacle from the current sampling time to the previous sampling time.

Warning method of obstacles and device of obstacles

Embodiments of the present invention relate to method and apparatus for video anti-shaking. There is disclosed a method for use in a video anti-shaking, especially for real-time digital image stabilization, the method comprises: dividing at least one part of a current frame of a video into a plurality of regions (S); performing a progressive local motion detection for each of the plurality of regions to determine a local motion vector for the region (S, S, S); and determining a global motion vector for the current frame based on the local motion vectors for the plurality of regions (S). There is also described a corresponding apparatus and user equipment. With embodiments of the present invention, the video anti-shaking processing can be done without any additional hardware. Moreover, embodiments of the present invention may quickly and effectively determine a motion vector and corresponding compensation.

Method and apparatus for video anti-shaking

Embodiments of the present invention provide a method and apparatus for image fusion. A method for image fusion according to the embodiments of the present invention comprises: obtaining multiple images for a same scene; and calculating a fused image of the multiple images based on Bayes analysis by using a kernel function.

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Beijing, China

An Jiang