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:
Disclosed herein a method and apparatus for recommending a table service based on image recognition. According to an embodiment of the present disclosure, there is provided a method for recommending a table service, including: receiving a table image that is captured in real time; acquiring, by using an artificial intelligence of a pre-learned learning model, table information that includes object information and food information of at least one table in the table image; and recommending, based on the table information, a service for each of the at least one table.
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.
Abstract:
An apparatus for providing an object image recognition includes a boundary extraction unit to extract a boundary of an object image. A feature extraction module extracts a center point of the object image and at least one local feature point from the extracted boundary and calculates each distance between the extracted center point and the extracted local feature point.
Abstract:
The present invention relates to a device and a method of detecting arbitrarily piled objects, and a device and a method for picking a detected object. The present invention may provide a device and a method of detecting an object, which extract a unique local characterized part of the object by using a visual sensor, detect an object region, and estimate a posture from the detected object region. Also the present invention may provide an object picking system capable of being applied to an actual production process, such as assembling or packaging, in a cell producing method.
Abstract:
Disclosed is a working method using a sensor, which increases recognition of a component to increase mounting of a component and enhancing productivity. The working method includes: extracting an object to be picked from a pile of objects using the sensor; picking the extracted object to move the picked object to a predetermined place; and estimating an angle of the moved object in the current position using the sensor. Accordingly, the working method can perform precise component recognition and posture estimation by two steps: a component picking step and a component recognition step, and effectively apply to a manufacturing line, thereby improving mounting of a component and enhancing productivity of a product.
Abstract:
Provided are a human-tracking method and a robot apparatus. The human-tracking method includes receiving an image frame including a color image and a depth image, determining whether user tracking was successful in a previous image frame, and determining a location of a user and a goal position to which a robot apparatus is to move based on the color image and the depth image in the image frame, when user tracking was successful in the previous frame. Accordingly, a current location of the user can be predicted from the depth image, user tracking can be quickly performed, and the user can be re-detected and tracked using user information acquired in user tracking when detection of the user fails due to obstacles or the like.