Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images

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 th...

Полное описание

Сохранить в:

Библиографические подробности
Главный автор:	Wetzel, Johannes (auth)
Формат:	Электронный ресурс Глава книги
Язык:	английский
Опубликовано:	Karlsruhe KIT Scientific Publishing 2022
Серии:	Forschungsberichte aus der Industriellen Informationstechnik 25
Предметы:	Electrical engineering 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
Online-ссылка:	OAPEN Library: download the publication OAPEN Library: description of the publication
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!

Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images по Wetzel, Johannes

Опубликовано 2022

DOAB: download the publication
DOAB: description of the publication

Электронный ресурс Глава книги

Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images

Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images по Wetzel, Johannes

Схожие документы