Multimodal group action clustering in meetings
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
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Tracking the activity of participants in a meeting
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CHI '06 Extended Abstracts on Human Factors in Computing Systems
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Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
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LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
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Image and Vision Computing
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
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ACM Computing Surveys (CSUR)
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AmI '09 Proceedings of the European Conference on Ambient Intelligence
Graphical models for multi-modal automatic video editing in meetings
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
IEEE Transactions on Information Technology in Biomedicine
Recognizing multi-user activities using wearable sensors in a smart home
Pervasive and Mobile Computing
Multimodal integration for meeting group action segmentation and recognition
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Towards computer understanding of human interactions
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Multi channel sequence processing
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
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MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
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We address the problem of recognizing sequences of human interaction patterns in meetings, with the goal of structuring them in semantic terms. The investigated patterns are inherently group-based (defined by the individual activities of meeting participants, and their interplay), and multimodal (as captured by cameras and microphones). By defining a proper set of individual actions, group actions can be modeled as a two-layer process, one that models basic individual activities from low-level audio-visual features, and another one that models the interactions. We propose a two-layer Hidden Markov Model (HMM) framework that implements such concept in a principled manner, and that has advantages over previous works. First, by decomposing the problem hierarchically, learning is performed on low-dimensional observation spaces, which results in simpler models. Second, our framework is easier to interpret, as both individual and group actions have a clear meaning, and thus easier to improve. Third, different HMM models can be used in each layer, to better reflect the nature of each subproblem. Our framework is general and extensible, and we illustrate it with a set of eight group actions, using a public five-hour meeting corpus. Experiments and comparison with a single-layer HMM baseline system show its validity.