Multimodal Joint Head Orientation Estimation in Interacting Groups via Proxemics and Interaction Dynamics
| Autor: | Hayley Hung, Stephanie Tan, David M. J. Tax |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Human head
Computer Networks and Communications business.industry Orientation (computer vision) Computer science Ecological validity Head (linguistics) Deep learning 020206 networking & telecommunications Context (language use) scene understanding 02 engineering and technology Space (commercial competition) Human-Computer Interaction head orientation estimation Proxemics Hardware and Architecture Human–computer interaction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence interaction dynamics business |
| Zdroj: | ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(1) |
| ISSN: | 2474-9567 |
| DOI: | 10.1145/3448122 |
| Popis: | Human head orientation estimation has been of interest because head orientation serves as a cue to directed social attention. Most existing approaches rely on visual and high-fidelity sensor inputs and deep learning strategies that do not consider the social context of unstructured and crowded mingling scenarios. We show that alternative inputs, like speaking status, body location, orientation, and acceleration contribute towards head orientation estimation. These are especially useful in crowded and in-the-wild settings where visual features are either uninformative due to occlusions or prohibitive to acquire due to physical space limitations and concerns of ecological validity. We argue that head orientation estimation in such social settings needs to account for the physically evolving interaction space formed by all the individuals in the group. To this end, we propose an LSTM-based head orientation estimation method that combines the hidden representations of the group members. Our framework jointly predicts head orientations of all group members and is applicable to groups of different sizes. We explain the contribution of different modalities to model performance in head orientation estimation. The proposed model outperforms baseline methods that do not explicitly consider the group context, and generalizes to an unseen dataset from a different social event. |
| Databáze: | OpenAIRE |
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