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:
Disclosed herein are a check-in apparatus and method. The apparatus includes a sound wave signal reception unit and an information processing unit. The sound wave signal reception unit receives a sound wave signal from a customer's mobile communication device, and restores an ID from the received sound wave signal. The information processing unit extracts customer information and check-in date and time information from the ID restored by the sound wave signal reception unit, generates check-in information by combining the extracted information with shop information and apparatus information stored in the check-in apparatus, and transmits the generated check-in information to a check-in server.
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.