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Joint Probabilistic People Detection in Overlapping Depth Images

Wetzel, Johannes; Laubenheimer, Astrid; Heizmann, Michael

Abstract:
Privacy-preserving high-quality people detection is a vital computer vision task for various indoor scenarios, e.g. people counting, customer behavior analysis, ambient assisted living or smart homes. In this work a novel approach for people detection in multiple overlapping depth images is proposed. We present a probabilistic framework utilizing a generative scene model to jointly exploit the multi-view image evidence, allowing us to detect people from arbitrary viewpoints. Our approach makes use of mean-field variational inference to not only estimate the maximum a posteriori (MAP) state but to also approximate the posterior probability distribution of people present in the scene. Evaluation shows state-of-the-art results on a novel data set for indoor people detection and tracking in depth images from the top-view with high perspective distortions. Furthermore it can be demonstrated that our approach (compared to the the mono-view setup) successfully exploits the multi-view image evidence and robustly converges in only a few iterations.

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Originalveröffentlichung
DOI: 10.1109/ACCESS.2020.2972055
Zugehörige Institution(en) am KIT Institut für Industrielle Informationstechnik (IIIT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2169-3536
KITopen-ID: 1000117648
Erschienen in IEEE access
Band 8
Seiten 28349–28359
Vorab online veröffentlicht am 06.02.2020
Nachgewiesen in Scopus
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