Video and Multimedia Technologies Research

Video and multimedia content is now a part of one's daily life in many ways: television broadcasts, streamed online clips and movies, consumer videos of personal events, and even emails. As the amount of multimedia available continues to grow, the organization and discovery of this content increasingly needs automatic tools.

The Video and Multimedia Technologies Research Department (part of the Networking & IP Services Lab within AT&T Labs Research) is continually investigating innovations that improve all interactions with video and multimedia, whether it's on a desktop PC, a mobile device, or an in-home television. To learn more about this work, click on a link to a project, either currently active and historical or one of the general areas below for more information.

Visual API - Algorithms for Visual Intelligence in mobile and enterprise environments alike.
Miracle / CAE - Large-scale content acquisition and analysis engine framework.
VidCat - Simplified personal photo and video managmenet.
iMiracle - Mobile retrieval and playback, models customized by metadata and content.
CAE Services - HTTP interfaces to processing, indexing, and retrieval.
eClips - Personalized content alerts with relevance feedback for fast learning.
Content-Based Copy Detection - Discover content copies to enhance indexing and retrieval.
Visual Biometrics - Improve indexing with robust face- and lip-based recognition.
Real-time Multimedia Analysis - Enabling real-time audio-visual metadata streams in a small footprint.
Semantic Concepts - Machine learning and visual semantic classifiers for fast content filtering.
Unsupervised Segmentation and Classification - Collate similar short-time event segments in unsupervised frameworks.
Standards - Incorporate innovations into new standards definitions.
CONSENT - Identifying bitstream errors from delivery and decoding.
Social TV - Enhance your live TV experience with crowd intelligence.
CAM - Intelligent monitoring of multiple live metadata streams.
Summarization - Locate high-interest segments of video to identify unique content.