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...
Gorde:
| Egile nagusia: | |
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| Formatua: | Baliabide elektronikoa Liburu kapitulua |
| Hizkuntza: | ingelesa |
| Argitaratua: |
Karlsruhe
KIT Scientific Publishing
2022
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| Saila: | Forschungsberichte aus der Industriellen Informationstechnik
25 |
| Gaiak: | |
| Sarrera elektronikoa: | OAPEN Library: download the publication OAPEN Library: description of the publication |
| Etiketak: |
Etiketa erantsi
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| Gaia: | 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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| Deskribapen fisikoa: | 1 electronic resource (204 p.) |
| ISBN: | KSP/1000144094 9783731511779 |
| Sartu: | Open Access |