Amazon Technologies, Inc.

Nxp B.v.

Manivel Sethu

Yash Pandya

Lokesh Amarnath Ravindranathan

Abhinav Aggarwal

Kaustav Nandy

Laxmi Shivaji Ahire

Yongjun Wu

Muhammad Raffay Hamid

Nihal Shandilya

Shivam Agarwal

Imran Khan

Francois Martin

Pradeep Muruganandam

Heena Bansal

Sudeendra Maddur Gundurao

Systems and techniques for generation and curation of a professional headshot from a set of image data. The systems and techniques images from the set of image data based on characteristics of the representation of the individual within the image. The systems and techniques further include determining a bounding box to define a headshot, the bounding box determined based on guidelines established by heuristics and/or machine learning algorithms trained using data labeled based on heuristics.

Headshot extraction and curation

Some implementations include methods generating visualization emphasis object for players and may include receiving a video clip associated with a sporting event participated by a plurality of players using a playing field with a play object. The players in a frame of the video clip may be detected. Players who are on the playing field may be identified from the detected players. Each of the players identified to be on the playing field may be associated with a rectangular bounding box that provides an outline of each of the players. A player who has possession of the play object may be identified. A visualization emphasis object may be generated and placed on the player who has possession of the play object. The visual emphasis object may have a size proportional to height of a bounding box associated with the player having the possession of the play object.

Player spotlight

Techniques for reducing the latency of annotating a replay video segment may include receiving a video segment with content involving multiple individuals. An annotation task is performed concurrently with a tracking task. The annotation task receives annotation data to indicate which of the individuals is an individual of interest in a subset of frames of the video segment, and the tracking task tracks the individuals in the video segment by generating bounding objects corresponding to the individuals. The annotation data can be associated with the bounding objects to detect a bounding object for the individual of interest, and a visualization emphasis object is generated based on the detected bounding object in a replay video segment to identify the individual of interest.

Low-latency spotlighting

Systems and techniques for ranking and selection of headshots from a collection of images. The systems and techniques for ranking use heuristics to rank extracted headshots from a set of images based on features of the faces within the images. The ranking may be used to generate a training dataset for a machine learning model to determine quality scores for faces within images. The ranked images may then be stored for later access in reference to a video or set of images containing the individual.

Automated headshot ranking for retrieval and display

A system can be utilized to determine media clips for media content based on a comparison between subtitles of the media content and information associated with a third-party system. The comparisons can be utilized to identify subtitles based on the comparison and determine the media clips based on portions of the media content associated with the identified subtitles. A smoothing algorithm can be applied to the media clips to modify start times and end times of the media clips. The system can cause the media clips or the corresponding media content to be output by an external device.

Media content segment generation and presentation

Devices, systems, and methods are provided for enhanced video annotations using image analysis. A method may include identifying, by a first device, first faces of first video frames, and second faces of second video frames. The method may include determining a first score for the first video frames, the first score indicative of a first number of faces to label, the first number of faces represented by the first video frames, and determining a second score for the second video frames, the second score indicative of a second number of faces to label. The method may include selecting the first video frames for face labeling, and receiving a first face label for the first face. The method may include generating a second face label for the second faces. The method may include sending the first face label and the second face label to a second device for presentation.

Enhanced video annotation using image analysis

Systems, methods, and computer-readable media are disclosed for systems and methods for automated identification and tagging of video content. Example methods may include determining a first set of frames in video content, determining a first set of faces that appear in the first set of frames, and extracting first image content corresponding to respective faces of the first set of faces. Methods may include classifying the first image content into a first set of clusters, generating a second set of clusters comprising a second set of faces, wherein the second set of faces has less faces than the first set of faces, and determining a first actor identifier associated with a first face in the second set of clusters. Some methods may include determining second image content in the second set of clusters comprising the first face, and automatically associating the first actor identifier with the second image content.

Automated identification and tagging of video content

Methods and apparatus are described for automating aspects of the annotation of a media presentation. Events are identified that relate to entities associated with the scenes of the media presentation. These events are time coded relative to the media timeline of the media presentation and might represent, for example, the appearance of a particular cast member or playback of a particular music track. The video frames of the media presentation are processed to identify visually similar intervals that may serve as or be used to identify contexts (e.g., scenes) within the media presentation. Relationships between the event data and the visually similar intervals or contexts are used to identify portions of the media presentation during which the occurrence of additional meaningful events is unlikely. This information may be surfaced to a human operator tasked with annotating the content as an indication that part of the media presentation may be skipped.

Techniques for annotating media content

Video decoder apparatus and method, for decoding a motion-compensated transform-coded video stream. The apparatus has: a first mode in which it is operable to decode the video stream at a first resolution, and wherein a motion-compensation step of the decoding is performed in the image-domain. It also has a second mode in which it is operable to decode the video stream at a second resolution, and wherein at least one motion-compensation step is performed in the transform-domain. The decoder is adapted to switch between the two modes while decoding the video stream.

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Bengaluru, India

Manivel Sethu