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...
Αποθηκεύτηκε σε:
| Κύριος συγγραφέας: | |
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| Μορφή: | Online |
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
KIT Scientific Publishing
2022
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| Θέματα: | |
| Διαθέσιμο Online: | ONIX_20220718_9783731511779_115 |
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| Περίληψη: | 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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