Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images
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 th...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Karlsruhe
KIT Scientific Publishing
2022
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| Colección: | Forschungsberichte aus der Industriellen Informationstechnik
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009670730406719 |
| Sumario: | 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. |
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| Descripción Física: | 1 electronic resource (204 p.) |
