Human-Centred Intelligent Human Computer Interaction (HCI²): how far are we from attaining it?
International Journal of Autonomous and Adaptive Communications Systems
Towards a Subject-Centered Analysis for Automated Video Surveillance
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Implicit human-centered tagging
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Cluster-based distributed face tracking in camera networks
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
An efficient continuous tracking system in real-time surveillance application
Journal of Network and Computer Applications
International Journal of Computer Vision
Tracking the saliency features in images based on human observation statistics
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Head direction estimation from low resolution images with scene adaptation
Computer Vision and Image Understanding
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We define and address the problem of finding the {\em visual focus of attention for a varying number of wandering people} (VFOA-W) -- where the people's movement is unconstrained. VFOA-W estimation is a new and important problem with mplications for behavior understanding and cognitive science, as well as real-world applications. One such application, which we present in this article, monitors the attention passers-by pay to an outdoor advertisement. Our approach to the VFOA-W problem proposes a multi-person tracking solution based on a dynamic Bayesian network that simultaneously infers the (variable) number of people in a scene, their body locations, their head locations, and their head pose. For efficient inference in the resulting large variable-dimensional state-space we propose a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampling scheme, as well as a novel global observation model which determines the number of people in the scene and localizes them. We propose a Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM)-based VFOA-W model which use head pose and location information to determine people's focus state. Our models are evaluated for tracking performance and ability to recognize people looking at an outdoor advertisement, with results indicating good performance on sequences where a moderate number of people pass in front of an advertisement.