Conference Paper/Proceeding/Abstract 798 views 459 downloads
Detecting humans in RGB-D data with CNNs
Kaiyang Zhou,
Adeline Paiement,
Majid Mirmehdi
Pages: 306 - 309
Swansea University Author: Adeline Paiement
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DOI (Published version): 10.23919/MVA.2017.7986862
Abstract
We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two color and depth CNNs. To combine the detections produced by the two CNNs, we propose a novel fusion approach based on the...
| Published: |
The Fifteenth IAPR International Conference on Machine Vision Applications
2017
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| Online Access: |
http://www.mva-org.jp/mva2017/ |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa31971 |
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| Abstract: |
We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two color and depth CNNs. To combine the detections produced by the two CNNs, we propose a novel fusion approach based on the characteristics of depth images. We also present a new depth-encoding scheme, which not only encodes depth images into three channels but also enhances the information for classification. We conduct experiments on a publicly available RGB-D people dataset and show that our approach outperforms the baseline models that only use RGB data. |
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| College: |
Faculty of Science and Engineering |
| Start Page: |
306 |
| End Page: |
309 |