Disclosed herein are systems and methods for presenting an audio signal associated with presentation of a virtual object colliding with a surface. The virtual object and the surface may be associated with a mixed reality environment. Generation of the audio signal may be based on at least one of an audio stream from a microphone and a video stream form a sensor. In some embodiments, the collision between the virtual object and the surface is associated with a footstep on the surface.

A recording system includes a recording data acquisition unit, an analysis unit, a learning unit, and a determination unit. The recording data acquisition unit acquires recording data in which a surrounding situation of a vehicle is recorded. The analysis unit performs image analysis processing on video data included in the recording data to create information regarding a transition of a color component in the video data. The learning unit learns the transition of the color component using artificial intelligence to create a trained model used to determine an occurrence of an event. The determination unit determines whether an event occurs using the trained model on a basis of the recording data. When the determination unit determines the occurrence of the event, the event recording control unit causes a recording unit to record, as event recording data, the recording data including a time point upon the occurrence of the event.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a driving scenario machine learning network and providing a simulated driving environment. One of the operations is performed by receiving video data that includes multiple video frames depicting an aerial view of vehicles moving about an area. The video data is processed and driving scenario data is generated which includes information about the dynamic objects identified in the video. A machine learning network is trained using the generated driving scenario data. A 3-dimensional simulated environment is provided which is configured to allow an autonomous vehicle to interact with one or more of the dynamic objects.

Identifying activity in an area even during periods of poor visibility using a video camera and an audio sensor are disclosed. The video camera is used to identify visible events of interest and the audio sensor is used to capture audio occurring temporally with the identified visible events of interest. A sound profile is determined for each of the identified visible events of interest based on sounds captured by the audio sensor during the corresponding identified visible event of interest. Then, during a time of poor visibility, a subsequent sound event is identified in a subsequent audio stream captured by the audio sensor. One or more sound characteristics of the subsequent sound event are compared with the sound profiles associated with each of the identified visible events of interest, and if there is a match, one or more matching sound profiles are filtered out from the subsequent audio stream.

A volumetric content enhancement system (“the system”) can annotate at least a portion of a plurality of voxels from a volumetric video with contextual data. The system can determine at least one actionable position within the volumetric video. The system can create an annotated volumetric video that includes the volumetric video, an annotation with the contextual data, and the at least one actionable position. The system can provide the annotated volumetric video to a volumetric content playback system. The system can obtain viewer feedback associated with the viewer and can determine an emotional state of the viewer based, at least in part, upon the viewer feedback. The system can receive viewer position information that identifies a specific actionable position of the viewer. The system can generate manipulation instructions to instruct the volumetric content playback system to manipulate the annotated volumetric content to achieve a desired emotional state of the viewer.

Embodiments of the application provide methods and devices for analyzing surgeries. It may include recording images of a surgery with a camera, wherein the images may include a visual element chosen from a surgeon's hands during the surgery, a patient's surgery area, equipment used in a surgery, instruments used in a surgery and the like; saving images of a surgery; displaying a timestamp in images; chapterizing the images into different chapters; leveraging additional radiologic imaging clinical data; maximizing treatment cost/benefit; and perhaps even analyzing recorded images of a surgery. Embodiments may use artificial intelligence, computer learning, and machine learning.

In some embodiments, the system is directed to an autonomous inspection system for electrical grid components. In some embodiments, the system collects electrical grid component data using an autonomous drone and then transmits the inspection data to one or more computers. In some embodiments, the system includes artificial intelligence that analysis the data and identifies electrical grid components defects and provides a model highlighting the defects to a user. In some embodiments, the system enables a user to train the artificial intelligence by providing feedback for models where defects or components are not properly identified.

A method for action recognition in a video is described. The method includes inputting a plurality of consecutive clips divided from the video into a convolutional neural network (CNN), and obtaining a set of clip descriptors; processing the set of clip descriptors via a Bi-directional Attention mechanism, and obtaining a global representation of the video; and performing video-classification for the global representation of the video such that action recognition is achieved.

An information processing apparatus acquires editing target image data including video image data, acquires first display image data of a plurality of frames, generates second display image data of a plurality of frames determined in accordance with the number of frames of the first display image data from the editing target image data, and displays display images of a plurality of frames indicated by the second display image data of the plurality of frames on a display. The editing target image data and the first display image data of the plurality of frames are image data having a common attribute. The second display image data of the plurality of frames includes second display image data for video images of a plurality of frames corresponding to still image data of a plurality of frames constituting at least a part of the video image data.

Techniques for prioritization of media related to an incident are provided. Confirmed incident related media may be retrieved, the confirmed incident related media having been confirmed as being associated with the incident. Artifacts of interest may be identified in the confirmed incident related media. Presence of the artifacts of interest in a plurality of received media may be determined. The plurality of received media may be prioritized based on the presence of the artifacts of interest.