Tracking and data association
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
SCAAT: incremental tracking with incomplete information
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic data association methods in visual tracking of groups
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multi-level Particle Filter Fusion of Features and Cues for Audio-Visual Person Tracking
Multimodal Technologies for Perception of Humans
Multi- and single view multiperson tracking for smart room environments
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Robot algorithms for localization of multiple emission sources
ACM Computing Surveys (CSUR)
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In prior work, we developed a speaker tracking system based on an extended Kalman filter using time delays of arrival (TDOAs) as acoustic features. In particular, the TDOAs comprised the observation associated with an iterated extended Kalman filter (IEKF) whose state corresponds to the speaker position. In other work, we followed the same approach to develop a system that could use both audio and video information to track a moving lecturer. While these systems functioned well, their utility was limited to scenarios in which a single speaker was to be tracked. In this work, we seek to remove this restriction by generalizing the IEKF, first to a probabilistic data association filter, which incorporates a clutter model for rejection of spurious acoustic events, and then to a joint probabilistic data association filter (JPDAF), which maintains a separate state vector for each active speaker. In a set of experiments conducted on seminar and meeting data, we demonstrate that the JPDAF provides tracking performance superior to the IEKF.