| Autor: |
Crispim-Junior CF; STARS team - INRIA Sophia Antipolis Mediterranee, Valbonne, France., Buso V; LABRI - University of Bordeaux, Talence, France., Avgerinakis K; CERTH - ITI, Thessaloniki, Greece., Meditskos G; CERTH - ITI, Thessaloniki, Greece., Briassouli A; CERTH - ITI, Thessaloniki, Greece., Benois-Pineau J; LABRI - University of Bordeaux, Talence, France., Kompatsiaris IY; CERTH - ITI, Thessaloniki, Greece., Bremond F; STARS team - INRIA Sophia Antipolis Mediterranee, Valbonne, France. |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE transactions on pattern analysis and machine intelligence [IEEE Trans Pattern Anal Mach Intell] 2016 Aug; Vol. 38 (8), pp. 1598-1611. Date of Electronic Publication: 2016 Mar 02. |
| DOI: |
10.1109/TPAMI.2016.2537323 |
| Abstrakt: |
Combining multimodal concept streams from heterogeneous sensors is a problem superficially explored for activity recognition. Most studies explore simple sensors in nearly perfect conditions, where temporal synchronization is guaranteed. Sophisticated fusion schemes adopt problem-specific graphical representations of events that are generally deeply linked with their training data and focused on a single sensor. This paper proposes a hybrid framework between knowledge-driven and probabilistic-driven methods for event representation and recognition. It separates semantic modeling from raw sensor data by using an intermediate semantic representation, namely concepts. It introduces an algorithm for sensor alignment that uses concept similarity as a surrogate for the inaccurate temporal information of real life scenarios. Finally, it proposes the combined use of an ontology language, to overcome the rigidity of previous approaches at model definition, and a probabilistic interpretation for ontological models, which equips the framework with a mechanism to handle noisy and ambiguous concept observations, an ability that most knowledge-driven methods lack. We evaluate our contributions in multimodal recordings of elderly people carrying out IADLs. Results demonstrated that the proposed framework outperforms baseline methods both in event recognition performance and in delimiting the temporal boundaries of event instances. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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