02016nam 2200433z- 450 9910584592103321202207191000144094(CKB)5580000000346213(oapen)https://directory.doabooks.org/handle/20.500.12854/90072(oapen)doab90072(EXLCZ)99558000000034621320202207d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierProbabilistic Models and Inference for Multi-View People Detection in Overlapping Depth ImagesKarlsruheKIT Scientific Publishing20221 online resource (204 p.)Forschungsberichte aus der Industriellen Informationstechnik3-7315-1177-0 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.Electrical engineeringbicsscdepth sensor indoor surveillanceinverses Problemjoint multi-view person detectionmean-field variational inferenceNetzwerk von 3D-Sensorenprobabilistische PersonendetektionTiefenbildervertical top-view indoor pedestrian detectionElectrical engineeringWetzel Johannesauth1306799BOOK9910584592103321Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images3028589UNINA