LEADER 02016nam  2200433z- 450 
001 9910584592103321
005 20220719
010   $a1000144094
035   $a(CKB)5580000000346213
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/90072
035   $a(oapen)doab90072
035   $a(EXLCZ)995580000000346213
100   $a20202207d2022      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aProbabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images
210   $aKarlsruhe$cKIT Scientific Publishing$d2022
215   $a1 online resource (204 p.)
225 1 $aForschungsberichte aus der Industriellen Informationstechnik
311 08$a3-7315-1177-0 
330   $aIn 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.
606   $aElectrical engineering$2bicssc
610   $adepth sensor indoor surveillance
610   $ainverses Problem
610   $ajoint multi-view person detection
610   $amean-field variational inference
610   $aNetzwerk von 3D-Sensoren
610   $aprobabilistische Personendetektion
610   $aTiefenbilder
610   $avertical top-view indoor pedestrian detection
615  7$aElectrical engineering
700   $aWetzel$b Johannes$4auth$01306799
906   $aBOOK
912   $a9910584592103321
996   $aProbabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images$93028589
997   $aUNINA