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 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.

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 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: An apparatus for rapidly detecting an object of interest includes: a first object of interest detector configured to determine a region of an object of interest for an image, from which the object of interest is to be detected, by using a first training image; and a second object of interest detector configured to detect the object of interest from the region of the object of interest determined by the first object of interest detector by using a second training image, which is bigger in size than the first training image.

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: Disclosed herein is an apparatus and method for detecting a person from an input video image with high reliability by using gradient-based feature vectors and a neural network. The human detection apparatus includes an image preprocessing unit for modeling a background image from an input image. A moving object area setting unit sets a moving object area in which motion is present by obtaining a difference between the input image and the background image. A human region detection unit extracts gradient-based feature vectors for a whole body and an upper body from the moving object area, and detects a human region in which a person is present by using the gradient-based feature vectors for the whole body and the upper body as input of a neural network classifier. A decision unit decides whether an object in the detected human region is a person or a non-person.

Abstract: Disclosed herein are an apparatus and method for recognizing a human in an image. The apparatus includes a learning unit and a human recognition unit. The learning unit calculates a boundary value between a human and a non-human based on feature candidates extracted from a learning image, detects a feature candidate for which an error is minimized as the learning image is divided into the human and the non-human using the calculated boundary value, and determines the detected feature candidate to be a feature. The human recognition unit extracts a candidate image where a human may be present from an acquired image, and determines whether the candidate image corresponds to a human based on the feature that is determined by the learning unit.

Abstract: A method and an apparatus for communicating wireless videos based on ZigBee network are provided. The method includes: receiving, a video transmitting apparatus, a video transmitting request from a user through a button input apparatus; receiving videos from a video input apparatus when an interrupt is generated and storing the received videos in a transmitting buffer, by setting a timer; and when data exceeding a predetermined capacity of the transmitting buffer are stored in the transmitting buffer, splitting the videos stored in the transmitting buffer into ZigBee packets and transmitting the split videos to a video receiving apparatus. According to the present invention, the video transmitting apparatus and the video receiving apparatus can control a power supply for peripheral apparatuses such as a video input apparatus, a video output apparatus, by a message, if necessary, and can minimize power consumed by the peripheral apparatuses.