An electronic device according to the present invention includes at least one memory and at least one processor which function as: an acquisition unit configured to acquire line-of-sight information relating to a line of sight of a user; a display control unit configured to execute control to display a captured image on a display surface; a tracking unit configured to detect an object in the image so as to track the object; and a setting unit configured to set a tracking target of the tracking unit, wherein the setting unit resets the tracking target based on the line-of-sight information and a tracking history of an object that is the tracking target in a case where the object stops being detected.

Described is a method of calibrating a camera. The method comprises obtaining geographical coordinates of a selected physical point location within an image view of the camera and measuring an angle between an x-axis of a real-world coordinate system passing through said selected point location with respect to true north. The method includes using said obtained geographical coordinates, said measured angle, and projection data derived from characteristics of the camera to derive modified projection data for transforming a two-dimensional pixel coordinate system of the camera image view into a three-dimensional geographical coordinate system for point locations within the image view of the camera.

A system for pedestrian re-identification comprises multiple cameras and a computing system. The cameras are configured to obtain data. The data comprises multiple images associated with one or more objects. The computing system is configured to extract features for each of the multiple images, determine a descriptor for each of the multiple images, and identify one or more images among the multiple images associated with an object among the one or more objects based on the descriptors of the multiple images. A respective descriptor for a respective image comprises a first set of units representing all the extracted features in the respective image and a second set of units representing a subset of the extracted features in the respective image.

Provided are an urban digital twin platform system and a moving object information analysis and management method therefor. Through the urban digital twin platform system and the moving object information analysis and management method, a moving object such as a vehicle or a pedestrian may be detected from multimodal sensor data, data on the moving object may be generated, and a situation may be quickly determined by deriving complex actions of the moving object. The urban digital twin platform system includes a multimodal sensor data input and objectification module configured to detect a moving object and to generate objectification data, a multimodal sensor data analysis module configured to classify basic actions of the moving object, to classify complex actions of the moving object, and to generate moving object information, and an urban space data server configured to store the objectification data and the moving object information.

A generative adversarial network. The generative adversarial network includes: a generator configured for generating an image and a corresponding label map; a discriminator configured for determining a classification of a provided image and a provided label map, wherein the classification characterizes whether the provided image and the provided label map have been generated by the generator or not and determining the classification comprises the steps of: determining a first feature map of the provided image; masking the first feature map according to the provided label map thereby determining a masked feature map; globally pooling the masked feature map thereby determining a feature representation of the provided image masked by the provided label map; determining a classification of the image based on the feature representation.

A device may obtain video data associated with a driving event involving a first vehicle. The device may determine a vanishing point associated with the video data and may construct a cone of impact of the first vehicle based on the vanishing point. The device may detect a second vehicle within the cone of impact and may analyze the subset of video frames to determine a distance between the first vehicle and the second vehicle. The device may determine a speed of the first vehicle during a time period associated with a subset of video frames. The device may determine a headway score, representative of a severity associated with the first vehicle being within a proximity threshold of the second vehicle during the time period, based on the distance and the speed. The device may determine an occurrence of a tailgating event based on the headway score.

A speed estimation system includes: a detection module configured to determine bounding boxes of an object moving on a surface in images, respectively, captured using a camera; a solver module configured to, based on the bounding boxes, determine a homography of the surface by solving an optimization problem, where the solver module is not trained; and a speed module configured to, using the homography, determine a speed that the object is moving on the surface.

A method for 2D semantic keypoint detection and tracking is described. The method includes learning embedded descriptors of salient object keypoints detected in previous images according to a descriptor embedding space model. The method also includes predicting, using a shared image encoder backbone, salient object keypoints within a current image of a video stream. The method further includes inferring an object represented by the predicted, salient object keypoints within the current image of the video stream. The method also includes tracking the inferred object by matching embedded descriptors of the predicted, salient object keypoints representing the inferred object within the previous images of the video stream based on the descriptor embedding space model.

Provided is an illumination control system including: an image capturing unit capturing an image in real time; a motion sensing unit sensing motion in the image and generating a notification; a communication unit receiving the image and the notification for transmission to outside, and receiving a control signal; a control unit receiving information on the image and the notification through the communication unit, analyzing the information, and transmitting the control signal for control; a light unit connected to the communication unit so that lights are controlled through the control signal; and a storage unit storing the information.

A method is provided for the spatial characterization of at least one vehicle image of image information, wherein the image information encompasses the vehicle image of an external vehicle and an environment image of an environment of the external vehicle. The method comprises: determining a bounding box for the vehicle image, in order to use the bounding box for a delimiting of the vehicle image from the environment image, determining a splitting line for the bounding box, in order to use the splitting line for a partitioning of the vehicle image into at least two vehicle sides, determining the spatial characterization with the aid of the bounding box and the splitting line, wherein at least one evaluation means based on machine learning, especially a neural network, is used for the determining of the bounding box and the splitting line.