Abstract:
A human information recognition method includes analyzing sensor data from multi-sensor resource placed in a recognition space to generate human information based on the sensor data, the human information including an identity, location and activity information of people existed in the recognition space. Further, the human information recognition method includes mixing the human information based on the sensor data with human information, the human information being acquired through interaction with the people existed in the recognition space; and storing a human model of the people existed in the recognition space depending on the mixed human information in a database unit.
Abstract:
Provided is a multiple-intelligence detection system. The multiple-intelligence detection system includes an image detection device obtaining image information for evaluating multiple-intelligence from a user, a multiple-intelligence measurement model unit receiving the image information from the image detection device to perform multiple-intelligence evaluation through selection of one of a first reaction and a second reaction, and a content unit receiving a result of the evaluated multiple-intelligence from the multiple-intelligence measurement model unit to generate an individual portfolio on the basis of the received result. The multiple-intelligence measurement model unit selects one of the first and second reactions on the basis of a reference reaction according to feelings and behavior patterns of the user.
Abstract:
An apparatus includes an image receiving module configured to collect a depth image provided from a camera, a human body detection module configured to detect a human body from the collected depth image, and an activity recognition module configured to recognize an action of the human body on the basis of a 3-dimensional action volume extracted from the human body and a previously learned action model.
Abstract:
A method for tracking an object in an object tracking apparatus includes receiving an image frame of an image; and detecting a target, a depth analogous obstacle and an appearance analogous obstacle; tracking the target, the depth analogous obstacle and the appearance analogous obstacle; when the detected target overlaps the depth analogous obstacle, comparing the variation of tracking score of the target with that of the depth analogous obstacle. Further, the method includes continuously tracking the target when the variation of tracking score of the target is below that of the depth analogous obstacle and processing a next frame when the variation of tracking score of the target is above that of the depth analogous obstacle; and re-detecting the target.