Electronics and Telecommunications Research Institute

Woo Han Yun

Hye Jin Kim

Jae Yeon Lee

Jae Hong Kim

Joo Chan Sohn

Ho Sub Yoon

Su Young Chi

Young Woo Yoon

Kye Kyung Kim

Mun Sung Han

Kyu Dae Ban

Do-Hyung Kim

Jong-Hyun Park

Jong Hyun Park

Jae Il Cho

Ho Sun Yoon

Yun Koo Chung

Young-Jo Cho

Dae Ha Lee

Suyoung Chi

Kyu-Dae Ban

Youngwoo Yoon

Disclosed are an apparatus for tracking a location of a hand, includes: a skin color image detector for detecting a skin color region from an image input from an image device using a predetermined skin color of a user; a face tracker for tracking a face using the detected skin color image; a motion detector for setting a ROI using location information of the tracked face, and detecting a motion image from the set ROI; a candidate region extractor for extracting a candidate region with respect to a hand of the user using the skin color image detected by the skin color image detector and the motion image detected by the motion detector; and a hand tracker for tracking a location of the hand in the extracted candidate region to find out a final location of the hand.

Apparatus and method for tracking hand

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.

Human-tracking method and robot apparatus for performing the same

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.

Apparatus and method for providing activity recognition based application service

Provided is a gesture recognition apparatus. The gesture recognition apparatus includes a human detection unit, a gesture region setting region, an arm detection unit and a gesture determination unit. The human detection unit detects a face region of a user from an input image. The gesture region setting unit sets a gesture region, in which a gesture of the user's arm occurs, with respect to the detected face region. The arm detection unit detects an arm region of the user in the gesture region. The gesture determination unit analyzes a position, moving directionality and shape information of the arm region in the gesture region to determine a target gesture of the user. Such a gesture recognition apparatus may be used as a useful means for a human-robot interaction in a long distance where a robot has difficulty in recognizing a user's voice.

Gesture recognition apparatus, robot system including the same and gesture recognition method using the same

Disclosed are a component recognizing apparatus and a component recognizing method. The component recognizing apparatus includes: an image preprocessing unit configured to extract component edges from an input component image by using a plurality of edge detecting techniques, and detect a component region by using the extracted component edges; a feature extracting unit configured to extract a component feature from the detected component region, and create a feature vector by using the component feature; and a component recognizing unit configured to input the created feature vector to an artificial neural network which has learned in advance to recognize a component category through a plurality of component image samples, and recognize the component category according to a result.

Component recognizing apparatus and component recognizing method

Disclosed is a method of detecting an upper body. The method includes detecting an omega candidate area including a shape formed of a face and a shoulder line of a human from a target image, cutting the target image into the upper body candidate area including the omega candidate area, detecting a human face from the upper body candidate area, and judging whether the upper body of the human is included in the target image according to the result of detecting the human face.

Apparatus and method for detecting upper body

The present invention detects a candidate ROI group associated with character strings/figure strings on the basis of a result acquired through prior learning of various types of license plates, verifies the interested region candidate group detected by using at least one condition of five predetermined conditions, and determines an MBR region in the selected ROI region from the verified interested region candidate group by considering a ratio between the height and width of the ROI region to recognize the license plate for the automobile. According to the present invention, it is possible to automatically detect the location of the license plate regardless of various types of license plate specifications defined for each of countries.

Apparatus and method for recognizing identifier of vehicle

Growing community of inventors

Daejeon, South Korea

Woo Han Yun