Explore
Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images
Johannes Wetzel
2022
0 Ungluers have
Faved this Work
Login to Fave
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.
This book is included in DOAB.
Why read this book? Have your say.
You must be logged in to comment.
Rights Information
Are you the author or publisher of this work? If so, you can claim it as yours by registering as an Unglue.it rights holder.Downloads
This work has been downloaded 39 times via unglue.it ebook links.
- 39 - pdf (CC BY-SA) at Unglue.it.
Keywords
- depth sensor indoor surveillance
- Electrical engineering
- Energy technology & engineering
- inverses Problem
- joint multi-view person detection
- mean-field variational inference
- Netzwerk von 3D-Sensoren
- probabilistische Personendetektion
- Technology, engineering, agriculture
- Tiefenbilder
- vertical top-view indoor pedestrian detection
Links
DOI: 10.5445/KSP/1000144094Editions