Abstract:
The present invention relates to an apparatus for recognizing intention of a horse-riding simulator user, and a method thereof, and the apparatus for recognizing intention of a horse-riding simulator user can provide a safe and realistic horse-riding simulation environment to a user by recognizing an aid signal and an intention signal of the user to sense a dangerous situation and accordingly coping with the situation. According to the present invention, it is possible to increase the sense of the real for the user by enabling the horse-riding simulator user to perform similar interaction to actual horse-riding, and to increase effects of horse-riding training using the horse-riding simulator. Particularly, there is an advantage that the dangerous situation is sensed for safe riding, and it is possible to contribute to formation of a related technology market by providing an effective method for recognition of the intention of the horse-riding simulator user.
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:
The present invention relates to a method and apparatus for re-training model by recognizing uncertainty in online user behavior detection. A method for training a model related to user behavior detection according to an embodiment of the present disclosure may comprise: detecting one or more behavior instances having a type of user behavior and a time interval of the user behavior; calculating at least one of a first uncertainty value for the type of user behavior or a second uncertainty value for the time interval of the user behavior; generating a first data newly labeling the type of user behavior or a second data newly labeling the time interval of the user behavior; and training a model that recognizes the user behavior information on a frame-by-frame basis.
Abstract:
Provided are a device and method for tagging training data. The method includes detecting and tracking one or more objects included in a video using artificial intelligence (AI), when there is an object to be split in a result of tracking the detected objects, splitting the object in object units, and when there are identical objects to be merged among split objects, merging the objects.
Abstract:
Provided are a device and method for tagging training data. The method includes detecting and tracking one or more objects included in a video using artificial intelligence (AI), when there is an object to be split in a result of tracking the detected objects, splitting the object in object units, and when there are identical objects to be merged among split objects, merging the objects.
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:
Disclosed herein a method and apparatus for learning a locally-adaptive local device task based on cloud simulation. According to an embodiment of the present disclosure, there is provided a method for learning a locally-adaptive local device task. The method comprising: receiving observation data about a surrounding environment recognized by a local device; performing a domain randomization based on the observation data and a failure type of a task assigned to the local device and relearning a policy network of the assigned task based on the domain randomization; and updating a policy network of the local device for the assigned task by transmitting the relearned policy network to the local device.
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:
The present invention provides a choreography searching system and method based on a motion inquiry which inputs a choreography video which is captured by a user at real time when the user dances in front of a camera and inputs the choreography video as an inquiry to compare the choreography with choreographic works such as K-POP stored in a choreography database to provide a list of choreographic works which are arranged in the order of similarity in order to provide intuitive choreography input based search rather than text based search such as a music title, a choreographer, or a name of a unit motion.