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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| Format: | eBook |
| Language: | English |
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Karlsruhe
KIT Scientific Publishing
2022
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| Series: | Forschungsberichte aus der Industriellen Informationstechnik
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| Online Access: | |
| Collection: | Directory of Open Access Books - Collection details see MPG.ReNa |
| Summary: | 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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| Item Description: | Creative Commons (cc), by-sa/4.0, http://creativecommons.org/licenses/by-sa/4.0 |
| Physical Description: | 1 electronic resource (204 p.) |
| ISBN: | 9783731511779 1000144094 |