Andere Kunden interessierten sich auch für
![Statistical Inference in Linear Models]( "Statistical Inference in Linear Models")
Statistical Inference in Linear Models88,99 €![Architectural, Civil, and Infrastructure Engineering in View of Sustainability]( "Architectural, Civil, and Infrastructure Engineering in View of Sustainability")
Architectural, Civil, and Infrastructure Engineering in View of Sustainability108,99 €![People to Meet]( "People to Meet")
People to Meet11,99 €![Attitudes and Awareness of Young People 18 - 25 to Reuse:]( "Attitudes and Awareness of Young People 18 - 25 to Reuse:")
Attitudes and Awareness of Young People 18 - 25 to Reuse:36,99 €![Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods]( "Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods")
Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods84,99 €![A Guide to Lab view Interfaced Arduino Projects]( "A Guide to Lab view Interfaced Arduino Projects")
A Guide to Lab view Interfaced Arduino Projects30,99 €![Probabilistic Safety Assessment Studies For Research Reactors]( "Probabilistic Safety Assessment Studies For Research Reactors")
Probabilistic Safety Assessment Studies For Research Reactors40,99 €
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.