Multimodal people ID for a multimedia meeting browser
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Tracking Focus of Attention in Meetings
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Face Cataloger: Multi-Scale Imaging for Relating Identity to Location
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Towards reliable multimodal sensing in aware environments
Proceedings of the 2001 workshop on Perceptive user interfaces
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
A GENERIC FACE REPRESENTATION APPROACH FOR LOCAL APPEARANCE BASED FACE VERIFICATION
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Audio-visual multi-person tracking and identification for smart environments
Proceedings of the 15th international conference on Multimedia
Multimodal Technologies for Perception of Humans
Multi-level Particle Filter Fusion of Features and Cues for Audio-Visual Person Tracking
Multimodal Technologies for Perception of Humans
Evaluating multiple object tracking performance: the CLEAR MOT metrics
Journal on Image and Video Processing - Regular
ISL person identification systems in the CLEAR evaluations
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An embedded audio-visual tracking and speech purification system on a dual-core processor platform
Microprocessors & Microsystems
Multimodal identification and tracking in smart environments
Personal and Ubiquitous Computing
| Hi-index | 0.00 |
In the context of smart environments, the ability to track and identify persons is a key factor, determining the scope and flexibility of analytical components or intelligent services that can be provided. While some amount of work has been done concerning the camera-based tracking of multiple users in a variety of scenarios, technologies for acoustic and visual identification, such as face or voice ID, are unfortunately still subjected to severe limitations when distantly placed sensors have to be used. Because of this, reliable cues for identification can be hard to obtain without user cooperation, especially when multiple users are involved. In this paper, we present a novel technique for the tracking and identification of multiple persons in a smart environment using distantly placed audio-visual sensors. The technique builds on the opportunistic integration of tracking as well as face and voice identification cues, gained from several cameras and microphones, whenever these cues can be captured with a sufficient degree of confidence. A probabilistic model is used to keep track of identified persons and update the belief in their identities whenever new observations can be made. The technique has been systematically evaluated on the CLEAR Interactive Seminar database, a large audio-visual corpus of realistic meeting scenarios captured in a variety of smart rooms.