Huynh, Tam ; Schiele, Bernt (2006)
Towards less supervision in activity recognition from wearable sensors.
10th IEEE International Symposium on Wearable Computers (ISWC 2006). Montreux, Switzerland (11.10.2006-14.10.2006)
doi: 10.1109/ISWC.2006.286336
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Activity Recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware wearable computing. However, most approaches for activity recognition rely on supervised learning techniques limiting their applicability in real-world scenarios and their scalability to large amounts of activities and training data. State-of-the-art activity recognition algorithms can roughly be divided in two groups concerning the choice of the classifier, one group using generative models and the other discriminative approaches. This paper presents a method for activity recognition which combines a generative model with a discriminative classifier in an integrated approach. The generative part of the algorithm allows to extract and learn structure in activity data without any labeling or supervision. The discriminant part then uses a small but labeled subset of the training data to train a discriminant classifier. In experiments we show that this scheme enables to attain high recognition rates even though only a subset of the training data is used for training. Also the tradeoff between labeling effort and recognition performance is analyzed and discussed.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2006 |
| Autor(en): | Huynh, Tam ; Schiele, Bernt |
| Art des Eintrags: | Bibliographie |
| Titel: | Towards less supervision in activity recognition from wearable sensors |
| Sprache: | Deutsch |
| Publikationsjahr: | 2006 |
| Ort: | Piscataway, N.J |
| Verlag: | IEEE Service Center |
| Buchtitel: | 2006 10th IEEE International Symposium on Wearable Computers |
| Veranstaltungstitel: | 10th IEEE International Symposium on Wearable Computers (ISWC 2006) |
| Veranstaltungsort: | Montreux, Switzerland |
| Veranstaltungsdatum: | 11.10.2006-14.10.2006 |
| DOI: | 10.1109/ISWC.2006.286336 |
| Kurzbeschreibung (Abstract): | Activity Recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware wearable computing. However, most approaches for activity recognition rely on supervised learning techniques limiting their applicability in real-world scenarios and their scalability to large amounts of activities and training data. State-of-the-art activity recognition algorithms can roughly be divided in two groups concerning the choice of the classifier, one group using generative models and the other discriminative approaches. This paper presents a method for activity recognition which combines a generative model with a discriminative classifier in an integrated approach. The generative part of the algorithm allows to extract and learn structure in activity data without any labeling or supervision. The discriminant part then uses a small but labeled subset of the training data to train a discriminant classifier. In experiments we show that this scheme enables to attain high recognition rates even though only a subset of the training data is used for training. Also the tradeoff between labeling effort and recognition performance is analyzed and discussed. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Multimodale Interaktive Systeme |
| Hinterlegungsdatum: | 20 Nov 2008 08:25 |
| Letzte Änderung: | 28 Nov 2024 10:44 |
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