About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ICME 2006
Conference paper
Person tracking in smart rooms using dynamic programming and adaptive subspace learning
Abstract
We present a robust vision system for single person tracking inside a smart room using multiple synchronized, calibrated, stationary cameras. The system consists of two main components, namely initialization and tracking, assisted by an additional component that detects tracking drift. The main novelty lies in the adaptive tracking mechanism that is based on subspace learning of the tracked person appearance in selected two-dimensional camera views. The subspace is learned on the fly, during tracking, but in contrast to the traditional literature approach, an additional "forgetting" mechanism is introduced, as a means to reduce drifting. The proposed algorithm replaces mean-shift tracking, previously employed in our work. By combining the proposed technique with a robust initialization component that is based on face detection and spatio-temporal dynamic programming, the resulting vision system significantly outperforms previously reported systems for the task of tracking the seminar presenter in data collected as part of the CHIL project. © 2006 IEEE.