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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| Superior document: | Forschungsberichte aus der Industriellen Informationstechnik |
|---|---|
| : | |
| Year of Publication: | 2022 |
| Language: | English |
| Series: | Forschungsberichte aus der Industriellen Informationstechnik
|
| Physical Description: | 1 electronic resource (204 p.) |
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| 100 | 1 | |a Wetzel, Johannes |4 auth | |
| 245 | 1 | 0 | |a Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images |
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| 490 | 1 | |a Forschungsberichte aus der Industriellen Informationstechnik | |
| 520 | |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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Electrical engineering |2 bicssc | |
| 653 | |a probabilistische Personendetektion | ||
| 653 | |a Netzwerk von 3D-Sensoren | ||
| 653 | |a Tiefenbilder | ||
| 653 | |a inverses Problem | ||
| 653 | |a joint multi-view person detection | ||
| 653 | |a depth sensor indoor surveillance | ||
| 653 | |a mean-field variational inference | ||
| 653 | |a vertical top-view indoor pedestrian detection | ||
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