Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.

Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.

Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.

Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.

Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.

Abstract: A multimedia entertainment system combines both gestures and voice commands to provide an enhanced control scheme. A user's body position or motion may be recognized as a gesture, and may be used to provide context to recognize user generated sounds, such as speech input. Likewise, speech input may be recognized as a voice command, and may be used to provide context to recognize a body position or motion as a gesture. Weights may be assigned to the inputs to facilitate processing. When a gesture is recognized, a limited set of voice commands associated with the recognized gesture are loaded for use. Further, additional sets of voice commands may be structured in a hierarchical manner such that speaking a voice command from one set of voice commands leads to the system loading a next set of voice commands.

Abstract: A method and system are disclosed in which a group of people are able to replicate the physical world experience of going with a group of friends to pick a movie, watch the movie together, and provide commentary on the movie itself in the virtual world on a virtual couch while each user is sitting in different physical locations. Additionally, the virtual representation of the destination that the group of people are watching the movie together in can be themed to allow users to watch movies in different locations pivoting on special events or by the users choice.

Abstract: Examples are disclosed herein that relate to depth imaging techniques using ultrasound One example provides an ultrasonic depth sensing system configured to, for an image frame, emit an ultrasonic pulse from each of a plurality of transducers, receive a reflection of each ultrasonic pulse at a microphone array, perform transmit beamforming and also receive beamforming computationally after receiving the reflections, form a depth image, and output the depth image for the image frame.

Abstract: A multimedia entertainment system combines both gestures and voice commands to provide an enhanced control scheme. A user's body position or motion may be recognized as a gesture, and may be used to provide context to recognize user generated sounds, such as speech input. Likewise, speech input may be recognized as a voice command, and may be used to provide context to recognize a body position or motion as a gesture. Weights may be assigned to the inputs to facilitate processing. When a gesture is recognized, a limited set of voice commands associated with the recognized gesture are loaded for use. Further, additional sets of voice commands may be structured in a hierarchical manner such that speaking a voice command from one set of voice commands leads to the system loading a next set of voice commands.

Abstract: A method and system are disclosed in which a group of people are able to replicate the physical world experience of going with a group of friends to pick a movie, watch the movie together, and provide commentary on the movie itself in the virtual world on a virtual couch while each user is sitting in different physical locations. Additionally, the virtual representation of the destination that the group of people are watching the movie together in can be themed to allow users to watch movies in different locations pivoting on special events or by the users choice.

Abstract: A method and system are disclosed in which a group of people are able to replicate the physical world experience of going with a group of friends to pick a movie, watch the movie together, and provide commentary on the movie itself in the virtual world on a virtual couch while each user is sitting in different physical locations. Additionally, the virtual representation of the destination that the group of people are watching the movie together in can be themed to allow users to watch movies in different locations pivoting on special events or by the users choice.

Abstract: Examples are disclosed herein that relate to depth imaging techniques using ultrasound One example provides an ultrasonic depth sensing system configured to, for an image frame, emit an ultrasonic pulse from each of a plurality of transducers, receive a reflection of each ultrasonic pulse at a microphone array, perform transmit beamforming and also receive beamforming computationally after receiving the reflections, form a depth image, and output the depth image for the image frame.

Abstract: A method and system are disclosed in which a group of people are able to replicate the physical world experience of going with a group of friends to pick a movie, watch the movie together, and provide commentary on the movie itself in the virtual world on a virtual couch while each user is sitting in different physical locations. Additionally, the virtual representation of the destination that the group of people are watching the movie together in can be themed to allow users to watch movies in different locations pivoting on special events or by the users choice.

Abstract: A method and system are disclosed in which a group of people are able to replicate the physical world experience of going with a group of friends to pick a movie, watch the movie together, and provide commentary on the movie itself in the virtual world on a virtual couch while each user is sitting in different physical locations. Additionally, the virtual representation of the destination that the group of people are watching the movie together in can be themed to allow users to watch movies in different locations pivoting on special events or by the users choice.

Abstract: A method and system are disclosed in which a group of people are able to replicate the physical world experience of going with a group of friends to pick a movie, watch the movie together, and provide commentary on the movie itself in the virtual world on a virtual couch while each user is sitting in different physical locations. Additionally, the virtual representation of the destination that the group of people are watching the movie together in can be themed to allow users to watch movies in different locations pivoting on special events or by the users choice.

Abstract: A method and system are disclosed in which a group of people are able to replicate the physical world experience of going with a group of friends to pick a movie, watch the movie together, and provide commentary on the movie itself in the virtual world on a virtual couch while each user is sitting in different physical locations. Additionally, the virtual representation of the destination that the group of people are watching the movie together in can be themed to allow users to watch movies in different locations pivoting on special events or by the users choice.

Abstract: A system for integrating local speech recognition with cloud-based speech recognition in order to provide an efficient natural user interface is described. In some embodiments, a computing device determines a direction associated with a particular person within an environment and generates an audio recording associated with the direction. The computing device then performs local speech recognition on the audio recording in order to detect a first utterance spoken by the particular person and to detect one or more keywords within the first utterance. The first utterance may be detected by applying voice activity detection techniques to the audio recording. The first utterance and the one or more keywords are subsequently transferred to a server which may identify speech sounds within the first utterance associated with the one or more keywords and adapt one or more speech recognition techniques based on the identified speech sounds.

Abstract: A system for integrating local speech recognition with cloud-based speech recognition in order to provide an efficient natural user interface is described. In some embodiments, a computing device determines a direction associated with a particular person within an environment and generates an audio recording associated with the direction. The computing device then performs local speech recognition on the audio recording in order to detect a first utterance spoken by the particular person and to detect one or more keywords within the first utterance. The first utterance may be detected by applying voice activity detection techniques to the audio recording. The first utterance and the one or more keywords are subsequently transferred to a server which may identify speech sounds within the first utterance associated with the one or more keywords and adapt one or more speech recognition techniques based on the identified speech sounds.

Abstract: Scalable empirical testing of media file playback utilizes test hooks in each media player to support simulated human interaction and playback monitoring. A media crawler catalogs media files accumulated in a media file database to create a wordlist. One or more scalable instances of media tester accesses the wordlist to select items of work linked to media files. Work items and/or operating modes of media tester specify test parameters such as performance profiles or further define testing such as specifying repetitious playback on one or more media players. Media files are downloaded to and played by a scalable number of media players. Playback performance is monitored, analyzed and reported. Failure reports are accompanied by instructions to reproduce failures and cross-references to content or source code in media files. Failures can be audited by additional work items for follow-up testing.

Abstract: A multimedia entertainment system combines both gestures and voice commands to provide an enhanced control scheme. A user's body position or motion may be recognized as a gesture, and may be used to provide context to recognize user generated sounds, such as speech input. Likewise, speech input may be recognized as a voice command, and may be used to provide context to recognize a body position or motion as a gesture. Weights may be assigned to the inputs to facilitate processing. When a gesture is recognized, a limited set of voice commands associated with the recognized gesture are loaded for use. Further, additional sets of voice commands may be structured in a hierarchical manner such that speaking a voice command from one set of voice commands leads to the system loading a next set of voice commands.