Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images
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Beschreibung
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.
Autor*in
Johannes Wetzel
Themen in »Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images«
probabilistische Personendetektion Netzwerk von 3D-Sensoren Tiefenbilder inverses Problem joint multi-view person detection depth sensor indoor surveillance mean-field variational inference vertical top-view indoor pedestrian detection