Samsung Electronics Co., Ltd.

Dae Hyun Ji

Dokwan Oh

Cheolhun Jang

Jaewoo Lee

Wonju Lee

DongWook Lee

Nayeon Kim

Yoonsuk Hyun

Jahoo Koo

Moonsub Byeon

Jinhyuk Choi

Seho Shin

ByeongJu Lee

Sung Hyun Chung

Yonggonjong Park

Disclosed is an apparatus and method with object detection. The method may include updating a pre-trained model based on sensing data of an image sensor, performing pseudo labeling using an interim model provided a respective training set, determining plural confidence thresholds based on an evaluation of the interim model, performing multiple trainings using the interim model and the generated pseudo labeled data, by applying the determined plural confidence thresholds to the multiple trainings, respectively, and generating an object detection model dependent on the performance of the multiple trainings, including generating an initial candidate object detection model when the interim model is the updated model.

Apparatus and method with object detection

A method and apparatus for determining a slope of a road using a side view camera of a vehicle, the method includes identifying a road image collected from a side view camera, dividing the road image into a plurality of regions, calculating a slope of a road for each of the plurality of regions based on driving lanes comprised in each of the plurality of regions, and determining a slope between the side view camera and the road using the slope of the road calculated for each of the plurality of regions.

Method and apparatus for determining slope of road using side view camera of vehicle

A method with distance estimation includes detecting a pedestrian region of a pedestrian comprised in a plurality of images received from a camera; determining a static point in the detected pedestrian region; and determining a distance between the pedestrian and the camera based on the static point in each of the images and a position of the camera corresponding to each of the images.

Method and apparatus with estimation of distance between pedestrian and camera

A method of generating lane information using a neural network includes generating a lane probability map based on an input image, generating lane feature information and depth feature information by applying the lane probability map to a second neural network, generating depth distribution information by applying the depth feature information to a third neural network, generating spatial information based on the lane feature information and the depth distribution information, generating offset information including a displacement between a position of a lane and a reference line by applying the spatial information to a fourth neural network, and generating three-dimensional (3D) lane information using the offset information.

Method and apparatus with lane generation

A method and apparatus for adjusting a neural network that classifies a scene of an input image into at least one class is provided. The method generates a feature image having a size that is less than a size of an input image by applying a convolutional network to the input image, determines at least one class corresponding to the feature image, generates a class image having a size corresponding to the size of the input image by applying a deconvolutional network to the feature image, calculates a loss of the class image based on a verification class image preset with respect to the input image, and adjusts the neural network based on the loss.

Image processing method and apparatus with neural network adjustment

An object positioning method and apparatus is disclosed. The object positioning apparatus may obtain a reference position of an object, obtain a map-based heading angle of the object based on waypoints on a map, and determine a current position of the object based on the reference position and the map-based heading angle.

Method and apparatus for determining object position

An object recognition method and apparatus are provided. The object recognition apparatus acquires localization information of a vehicle, acquires object information about an object located in front of the vehicle, determines a candidate region in which the object is predicted to exist in an image in front of the vehicle, based on the localization information and the object information, and recognizes the object in the image based on the candidate region.

Method and apparatus for recognizing object

Disclosed is a method and apparatus that includes acquiring a driving image; acquiring positioning information indicating a location of a vehicle; extracting map information corresponding to the positioning information; determining a regression line function corresponding to a road on which the vehicle is traveling based on the map information; detecting the linearity of the road from the driving image using the regression line function; and indicating the detected linearity.

Method and apparatus with linearity detection

A method and apparatus for controlling a vision sensor are provided. The apparatus and corresponding method are configured to predict an expected point, on a traveling path of a host vehicle, at which an illumination variation greater than or equal to a threshold is expected to occur, and determine whether the host vehicle is located within a threshold distance. The apparatus and corresponding method are also configured to control a vision sensor in the host vehicle based on the expected illumination variation in response to the host vehicle being located within the threshold distance.

Method and apparatus for controlling vision sensor for autonomous vehicle

Disclosed is a method and apparatus of detecting an object of interest, where the apparatus acquires an input image, sets a region of interest (ROI) in the input image, and detects the object of interest from a restoration image, having a resolution greater than a resolution of the input image, corresponding to the ROI.

Method and apparatus of detecting object of interest

A training data generation method includes acquiring an image captured from a vehicle and location information of the vehicle corresponding to the image; acquiring map data corresponding to the acquired location information; determining truth data including information associated with a road included in the image from the acquired map data; and generating training data including the image and the determined truth data.

Growing community of inventors

Hwaseong-si, South Korea

Dae Hyun Ji