Abstract: Disclosed is a water dispenser. The water dispenser includes a water outlet component; a water cooling module for accessing a water source, an output port of the water cooling module being communicated with the water outlet component through a first water pump; and/or a water heating module, an input port of the water heating module accessing the water source through a second water pump, an output port of the water heating module being communicated with the water outlet component, the water outlet component accessing the water source through the second water pump.

Abstract: A method includes receiving, at a conference endpoint, video captured using a wide angle lens. The method further includes selecting a view region in a frame of the video. The method further includes selectively applying, based on a size of the view region, deformation correction or distortion correction to the view region to generate a corrected video frame. The method further includes transmitting the corrected video frame to a remote endpoint.

Abstract: A method (1000) for operating cameras (202) in a cascaded network (100), comprising: capturing a first view (1200) with a first lens (326) having a first focal point (328) and a first centroid (352), the first view (1200) depicting a subject (1106); capturing a second view (1202) with a second lens (326) having a second focal point (328) and a second centroid (352); detecting a first location of the subject (1106), relative the first lens (326), wherein detecting the first location of the subject (1106), relative the first lens (326), is based on audio captured by a plurality of microphones (204); estimating a second location of the subject (1106), relative the second lens (326), based on the first location of the subject (1106) relative the first lens (326); selecting a portion (1206) of the second view (1202) as depicting the subject (1106) based on the estimate of the second location of the subject (1106) relative the second lens (326).

Abstract: A method of tracking a presenter during a videoconference, comprising: detecting, in a data stream, data indicating a presence of one or more persons at a videoconferencing endpoint, the data stream comprising a plurality of frames; detecting tracker initiation data (e.g., that a person is actively talking, or that a person has raised their hand, or both) in the data stream; determining a subject of interest responsive to the tracker initiation data, and tracking the subject of interest as they move while making a presentation.

Abstract: A videoconferencing endpoint is described that uses a cascading sequence of convolutional neural networks to perform face detection and upper body detection of participants in a videoconference at the endpoint, where at least one member of the sequence of neural networks performs upper body detection, and where the final member of the sequence of neural networks performs face detection based on the results of the upper body detection. The models of the neural networks are trained on both large datasets of faces well as images that have been distorted by a wide-angle camera of the videoconferencing endpoint.

Abstract: A method may include identifying primly camera speaker coordinates of a location of a speaker relative to a primary camera, converting the primary camera speaker coordinates to secondary camera speaker coordinates, the secondary camera speaker coordinates being of the location and relative to a secondary camera, and adjusting, using the secondary camera speaker coordinates of the secondary camera, a view setting of the secondary camera to track the location of the speaker.

Abstract: A method may include calculating a color gain by applying an automatic white balance (AWB) algorithm to a video frame of a video feed, calculating an illumination color by applying a machine learning model to the video frame, transforming the illumination color into an equivalent color gain, determining that a difference between the color gain and the equivalent color gain exceeds a difference threshold, reversing an effect of the illumination color on the video frame based on the threshold being exceeded to obtain a corrected video frame, and transmitting the corrected video frame to an endpoint.

Abstract: A method of tracking a presenter during a videoconference, comprising: detecting, in a data stream, data indicating a presence of one or more persons at a videoconferencing endpoint, the data stream comprising a plurality of frames; detecting tracker initiation data (e.g., that a person is actively talking, or that a person has raised their hand, or both) in the data stream; determining a subject of interest responsive to the tracker initiation data, and tracking the subject of interest as they move while making a presentation.

Abstract: A method (1000) for operating cameras (202) in a cascaded network (100), comprising: capturing a first view (1200) with a first lens (326) having a first focal point (328) and a first centroid (352), the first view (1200) depicting a subject (1106); capturing a second view (1202) with a second lens (326) having a second focal point (328) and a second centroid (352); detecting a first location of the subject (1106), relative the first lens (326), wherein detecting the first location of the subject (1106), relative the first lens (326), is based on audio captured by a plurality of microphones (204); estimating a second location of the subject (1106), relative the second lens (326), based on the first location of the subject (1106) relative the first lens (326); selecting a portion (1206) of the second view (1202) as depicting the subject (1106) based on the estimate of the second location of the subject (1106) relative the second lens (326).

Abstract: A videoconferencing endpoint is described that uses a combination of face detection, motion detection, and upper body detection for selecting participants of a videoconference for group framing. Motion detection is used to remove fake faces as well as to detect motion in regions around detected faces during postprocessing. Upper body detection is used in conjunction with the motion detection in postprocessing to allow saving faces that have been initially detected by face detection for group framing even if the participant has turned away from the camera, allowing the endpoint to keep tracking the participants region better than would be possible based only on an unstable result coming from face detection.

Abstract: A method for tracking movement of a presenter during a videoconference comprises tracking the location of subject of interest; quickly detecting error conditions—such as can occur when a subject of interest moves too quickly or when a subject of interest walks past another person—and quickly reinitiating the tracking sequence to help ensure a positive videoconferencing experience.

Abstract: A method for tracking movement of a presenter during a videoconference comprises tracking the location of subject of interest; quickly detecting error conditions—such as can occur when a subject of interest moves too quickly or when a subject of interest walks past another person—and quickly reinitiating the tracking sequence to help ensure a positive videoconferencing experience.

Abstract: A method includes receiving, at a conference endpoint, video captured using a wide angle lens. The method further includes selecting a view region in a frame of the video. The method further includes selectively applying, based on a size of the view region, deformation correction or distortion correction to the view region to generate a corrected video frame. The method further includes transmitting the corrected video frame to a remote endpoint.

Abstract: A videoconferencing endpoint is described that uses a combination of face detection, motion detection, and upper body detection for selecting participants of a videoconference for group framing. Motion detection is used to remove fake faces as well as to detect motion in regions around detected faces during postprocessing. Upper body detection is used in conjunction with the motion detection in postprocessing to allow saving faces that have been initially detected by face detection for group framing even if the participant has turned away from the camera, allowing the endpoint to keep tracking the participants region better than would be possible based only on an unstable result coming from face detection.

Abstract: A videoconferencing endpoint is described that uses a cascading sequence of convolutional neural networks to perform face detection and upper body detection of participants in a videoconference at the endpoint, where at least one member of the sequence of neural networks performs upper body detection, and where the final member of the sequence of neural networks performs face detection based on the results of the upper body detection. The models of the neural networks are trained on both large datasets of faces well as images that have been distorted by a wide-angle camera of the videoconferencing endpoint.

Abstract: A method may include receiving a source video frame captured using a wide angle lens, deriving a characteristic of a target video frame from the source video frame, generating a transitional video frame using the source video frame and the target video frame, and transmitting the transitional video frame to an endpoint.