Abstract: Method and apparatus for monitoring eating behavior are disclosed. A wearable device including an imaging sensor, an electromyography (EMG) sensor, and a processing unit is configured to collect EMG data via the EMG sensor, transmit the EMG data to a computing device, receive a control signal to capture one or more images via the imaging sensor, capture, one or more images, and transmit the one or more images. A computing device is disclosed including a processor and a memory containing a machine learning model and computer program code that, when executed, performs an operation. The operation includes receiving EMG data, processing the EMG data as input to a trained machine learning model, transmitting the control signal to capture one or more images via the imaging sensor, receiving the one or more images, and determining nutritional content of the food or beverage using the one or more images.

Abstract: The present teaching relates to analyzing user activities related to a video. The video is provided to a plurality of users. The plurality of users is monitored to detect one or more types of user activities performed in time with respect to different portions of the video. One or more visual representations of the monitored one or more types of user activities are generated. The one or more visual representations capture a level of attention paid by the plurality of users to the different portions of the video at any time instance. Interests of at least some of the plurality of users are determined with respect to the different portions of the video based on the one or more visual representations.

Abstract: Method and apparatus for monitoring eating behavior are disclosed. A wearable device including an imaging sensor, an electromyography (EMG) sensor, and a processing unit is configured to collect EMG data via the EMG sensor, transmit the EMG data to a computing device, receive a control signal to capture one or more images via the imaging sensor, capture, one or more images, and transmit the one or more images. A computing device is disclosed including a processor and a memory containing a machine learning model and computer program code that, when executed, performs an operation. The operation includes receiving EMG data, processing the EMG data as input to a trained machine learning model, transmitting the control signal to capture one or more images via the imaging sensor, receiving the one or more images, and determining nutritional content of the food or beverage using the one or more images.

Abstract: At each of a plurality of unmanned aerial vehicles operating in a three-dimensional space, current relative locations of other ones of the plurality of unmanned aerial vehicles which are visible to each of the plurality of unmanned aerial vehicles are determined based on two-dimensional camera images from on-board dual-lens cameras. The current relative locations are wirelessly transmitted to a controller. At the plurality of unmanned aerial vehicles, specification of a path to be followed by each of the plurality of unmanned aerial vehicles is wirelessly received from the controller. At each of the plurality of unmanned aerial vehicles, on-board collision avoidance is carried out based on the two-dimensional camera images from the on-board dual-lens cameras, while following the path specification.

Abstract: A system and method for painting a structure. The system comprises: a computer vision processing system configured to obtain images of a target structure and generate first instructions signals for real time communication to an unmanned aerial vehicle (UAV) having a paint fluid dispensing system provided thereon; a control device at the UAV responsive to received the first instruction signals for controlling real time navigating of the UAV to a location at the target structure; and the computer vision processing system configured to generate second instruction signals for real time communication to the UAV, wherein the control device at the UAV is configured to automatically actuate the paint fluid dispensing system to apply a paint fluid at the location on the target structure in response, the received second instruction signals configuring the UAV to render a desired visual image on the target structure.

Abstract: At each of a plurality of unmanned aerial vehicles operating in a three-dimensional space, current relative locations of other ones of the plurality of unmanned aerial vehicles which are visible to each of the plurality of unmanned aerial vehicles are determined based on two-dimensional camera images from on-board dual-lens cameras. The current relative locations are wirelessly transmitted to a controller. At the plurality of unmanned aerial vehicles, specification of a path to be followed by each of the plurality of unmanned aerial vehicles is wirelessly received from the controller. At each of the plurality of unmanned aerial vehicles, on-board collision avoidance is carried out based on the two-dimensional camera images from the on-board dual-lens cameras, while following the path specification.

Abstract: A system and method for painting a structure. The system comprises: a computer vision processing system configured to obtain images of a target structure and generate first instructions signals for real time communication to an unmanned aerial vehicle (UAV) having a paint fluid dispensing system provided thereon; a control device at the UAV responsive to received the first instruction signals for controlling real time navigating of the UAV to a location at the target structure; and the computer vision processing system configured to generate second instruction signals for real time communication to the UAV, wherein the control device at the UAV is configured to automatically actuate the paint fluid dispensing system to apply a paint fluid at the location on the target structure in response, the received second instruction signals configuring the UAV to render a desired visual image on the target structure.

Abstract: The present teaching relates to analyzing user activities related to a video. The video is provided to a plurality of users. The plurality of users is monitored to detect one or more types of user activities performed in time with respect to different portions of the video. One or more visual representations of the monitored one or more types of user activities are generated. The one or more visual representations capture a level of attention paid by the plurality of users to the different portions of the video at any time instance. Interests of at least some of the plurality of users are determined with respect to the different portions of the video based on the one or more visual representations.