Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images
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| Autore: |
Wetzel Johannes
|
| Titolo: |
Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images
|
| Pubblicazione: | Karlsruhe, : KIT Scientific Publishing, 2022 |
| Descrizione fisica: | 1 online resource (204 p.) |
| Soggetto topico: | Electrical engineering |
| Soggetto non controllato: | depth sensor indoor surveillance |
| inverses Problem | |
| joint multi-view person detection | |
| mean-field variational inference | |
| Netzwerk von 3D-Sensoren | |
| probabilistische Personendetektion | |
| Tiefenbilder | |
| vertical top-view indoor pedestrian detection | |
| Sommario/riassunto: | In this work, the task of wide-area indoor people detection in a network of depth sensors is examined. In particular, we investigate how the redundant and complementary multi-view information, including the temporal context, can be jointly leveraged to improve the detection performance. We recast the problem of multi-view people detection in overlapping depth images as an inverse problem and present a generative probabilistic framework to jointly exploit the temporal multi-view image evidence. |
| Titolo autorizzato: | Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images |
| ISBN: | 1000144094 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910584592103321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |