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220822 ||| eng |
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|a 9783731511779
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|a 1000144094
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|a Wetzel, Johannes
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| 245 |
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|a Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images
|h Elektronische Ressource
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| 260 |
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|a Karlsruhe
|b KIT Scientific Publishing
|c 2022
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| 300 |
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|a 1 electronic resource (204 p.)
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| 653 |
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|a depth sensor indoor surveillance
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| 653 |
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|a vertical top-view indoor pedestrian detection
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| 653 |
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|a Netzwerk von 3D-Sensoren
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| 653 |
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|a mean-field variational inference
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| 653 |
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|a joint multi-view person detection
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| 653 |
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|a Electrical engineering / bicssc
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| 653 |
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|a Tiefenbilder
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| 653 |
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|a probabilistische Personendetektion
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| 653 |
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|a inverses Problem
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 490 |
0 |
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|a Forschungsberichte aus der Industriellen Informationstechnik
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| 500 |
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|a Creative Commons (cc), by-sa/4.0, http://creativecommons.org/licenses/by-sa/4.0
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| 024 |
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|a 10.5445/KSP/1000144094
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| 856 |
4 |
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|u https://library.oapen.org/bitstream/20.500.12657/57538/1/9783731511779.pdf
|7 0
|x Verlag
|3 Volltext
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| 856 |
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|u https://directory.doabooks.org/handle/20.500.12854/90072
|z DOAB: description of the publication
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|a 620
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| 520 |
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|a 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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