Abstract:
A re-identification apparatus acquires a first image in which a tracking target entering an intersection is captured, and identifies the tracking target and targets having a predetermined positional relationship with the tracking target in the first image. The re-identification apparatus selects a camera to be used for re-identification of the tracking target based on a signal system of the intersection, and acquires a second image captured by the selected camera and one or more third images before or after the second image. The re-identification apparatus determines a target identified in the second image and the third images among the targets when identifying an object corresponding to the tracking target in the second image, and determines whether the re-identification of the tracking target is successful based on the targets identified in the first image and the target identified in the second image and the third images.
Abstract:
Provided is a technology for recognizing vehicle license plate information which includes detecting a position of a license plate from a vehicle image obtained by imaging a vehicle, extracting individual character images by separating a plurality of characters from a region of the detected license plate in the vehicle image and extracting a license plate type of the license plate according to a predetermined criterion, recognizing a license plate character string on the basis of the extracted individual character images and the license plate type and outputting the recognized license plate character string.
Abstract:
Provided is an apparatus and method for detecting a vehicle number plate that may determine whether an input image includes a number plate, based on an optimal feature to be used to determine whether the input image includes a number plate.
Abstract:
Provided is a method of optimizing parameter intervals of manufacturing processes based on prediction intervals. The method includes: collecting process data by applying an experiment design method to a target process; training a second-order polynomial regression model based on the collected process data; estimating importance values of each input variable with respect to each output variable using the second-order polynomial regression model; defining an objective function for process optimization based on the second-order polynomial regression model; optimizing each parameter value by applying an optimization algorithm to the defined objective function; and optimizing each parameter interval including the optimized parameter value in an input space using the prediction interval of the second-order polynomial regression model.
Abstract:
A method and apparatus for detecting and recognizing an object using a vector histogram based on a local binary pattern are disclosed. The apparatus of detecting and recognizing an object using a local binary pattern includes: a feature map creator configured to extract an object area in which a moving object exists from an input image, to create a local binary pattern by designating a local area in the object area, and to create a vector component map including information about magnitude vector components and direction vector components using the local binary pattern; a feature map configuring unit configured to divide the object area into a plurality of blocks and to create a feature vector map through a histogram using the vector component map in a unit of the block; and an object detector configured to detect and classify the moving object based on the feature vector map.
Abstract:
The present invention is directed to solving the existing problems and provides an apparatus and method for optimizing object detection performance by re-learning data specific to an installed location from an online server using a localization module in an edge terminal receiving a fixed image like a closed circuit television (CCTV) camera.
Abstract:
Provided is a method of creating a local database for local optimization of an object detector based on a deep neural network. The method includes performing preprocessing on an image extracted from real-time collected or pre-collected images from an edge device, modeling a static background image based on the image received through the pre-processing unit and calculating a difference image between a current input image and a background model to model a dynamic foreground image, detecting an object image from the image based on a training model, and creating a local database based on the background image, the foreground image synthesized with the background image, and the object image synthesized with the background image.