A system for motion-based media creation includes an article of footwear or apparel having at least one accelerometer or inertial measurement unit operative to monitor spatial motion of at least a portion of the article of footwear or apparel and generate a data stream indicative of the monitored spatial motion. The system further includes a processor in networked wireless communication with the article of footwear or apparel. The processor is configured to receive the data stream from the article of footwear or apparel; identify at least one motion primitive from the received data stream; and trigger the playback of an audio sample or a visual effect in response to the identified at least one motion primitive.

A method, system and computer program product for self-learned and probabilistic-based prediction of inter-camera object movement is disclosed. The method includes building and storing a transition model defined by transition probability and transition time distribution data generated during operation of a first video camera and one or more other video cameras over time. The method also includes employing at least one balance flow algorithm on the transition probability and transition time distribution data to determine a subset of the video cameras to initiate a search for an object based on a query. The method also includes running the search for the object over the subset of the video cameras.

Systems and methods of automatically extracting summaries of video content are described herein. A data processing system can access, from a video database, a first video content element including a first plurality of frame. The data processing system can select an intervallic subset of the first plurality of frames of the first video content element. The data processing system can calculate, for each of a plurality of further subsets comprising a predetermined number of frames from the intervallic subset, a score for the further subset. The data processing system can identify, from the plurality of further subsets, a further subset having a highest score. The data processing system can select a portion of the first video content element comprising the frames of the further subset having the highest score. The data processing system can generate a second video content element comprising the selected portion of the first video content element.

An input video sequence from a camera is filtered by a process that comprises detecting temporal tracks of moving image parts from the input video sequence and assigning activity scores to temporal segments of the tracks, using respective predefined track dependent activity score functions for a plurality of different activity types. Based on this, event scores for are computed as a function of time. This computation is controlled by a definition of a temporal sequence of activity types or compound activity types for an event type. Successive intermediate scores are computed, each as a function of time for a respective activity types or compound activity types in the temporal sequence. The successive intermediate scores for each respective activity types or compound activity are computed from a combination of the intermediate score for a preceding activity type or compound activity type in the temporal sequence at a preceding time and activity scores that were assigned to segments of the tracks after the preceding time, for the activity type or activity types defined by the compound activity type defined by the respective activity types or compound activity types in the temporal sequence. One of the computed event scores for a selected time. The computation of the selected event score is traced back to identify intermediate scores that were used to compute the selected one of the event scores and to identify segments of the tracks for which the assigned activity scores were used to compute the identified intermediate scores. An output video sequence and/or video image is generates that selectively includes the image parts associated with the selected segments.

A video processing system includes: an object movement information acquiring means for detecting a moving object moving in a plurality of segment regions from video data obtained by shooting a monitoring target area, and acquiring movement segment region information as object movement information, the movement segment region information representing segment regions where the detected moving object has moved; an object movement information and video data storing means for storing the object movement information in association with the video data corresponding to the object movement information; a retrieval condition inputting means for inputting a sequence of the segment regions as a retrieval condition; and a video data retrieving means for retrieving the object movement information in accordance with the retrieval condition and outputting video data stored in association with the retrieved object movement information, the object movement information being stored by the object movement information and video data storing means.

The disclosure relates to an artificial intelligence (AI) system using a machine learning algorithm such as deep learning, and an application thereof. In particular, an electronic apparatus, a document displaying method thereof, and a non-transitory computer readable recording medium are provided. An electronic apparatus according to an embodiment of the disclosure includes a display unit displaying a document, a microphone receiving a user voice, and a processor configured to acquire at least one topic from contents included in a plurality of pages constituting the document, recognize a voice input through the microphone, match the recognized voice with one of the acquired at least one topic, and control the display unit to display a page including the matched topic.

The disclosure includes a system and method for decomposing a video to salient fragments and synthesizing a video composition based on the salient fragments. A computer-implemented method receives a first set of salient fragments and a first set of clusters extracted from a video, where each cluster includes related salient fragments connected by a connectivity graph. The method determines a weight associated with each of the salient fragments and each of the clusters based on an activity level associated with the respective salient fragment or cluster and determine a permissible zone of activity. The method determines a spatial-temporal distortion to be applied to each salient fragment and cluster and synthesizes a video composition based on the first set of salient fragments, the first set of clusters and non-salient portions of the video using weighted editing.

The disclosure relates to an artificial intelligence (AI) system using a machine learning algorithm such as deep learning, and an application thereof. In particular, an electronic apparatus, a document displaying method thereof, and a non-transitory computer readable recording medium are provided. An electronic apparatus according to an embodiment of the disclosure includes a display unit displaying a document, a microphone receiving a user voice, and a processor configured to acquire at least one topic from contents included in a plurality of pages constituting the document, recognize a voice input through the microphone, match the recognized voice with one of the acquired at least one topic, and control the display unit to display a page including the matched topic.

Presentation of video highlights is disclosed. A data processing system receives from multiple users, multimedia files with user-generated video(s), the multimedia files being produced and enhanced by the users. The data processing system generates a speckle excitement vector of the multimedia files based on identifying feature(s) of the user-generated video(s). The processing and distribution system determines a cognitive state of each of the users based, in part, on the speckle excitement vector of each of the multimedia files. The processing and distribution system alters characteristic(s) of the user-generated video(s) of the multimedia files based on the cognitive state of each of the users that results in altered video(s). The processing and distribution system compiles the altered video(s) into a digital file that includes automatically-produced multimedia content. The processing and distribution system makes the digital file available for viewing.

In some embodiments, a method trains a first prediction network to predict similarity between images in videos. The training uses boundaries detected in the videos to train the prediction network to predict images in a same scene to have similar feature descriptors. The first prediction network generates feature descriptors that describe library images from videos in a video library offered to users of a video delivery service. A search image is received and the prediction network predicts one or more library images for one or more videos that are predicted to be similar to the received image. The one or more library images for the one or more videos are provided as a search result.