Multi-scale predictions fusion for robust hand detection and classification

In this paper, we present a multi-scale predictions fusion region-based Fully Convolutional Networks (MSPF-RFCN) to robustly detect and classify human hands under various challenging conditions. In our approach, the input image is passed through the proposed network to generate score maps, based on...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2019-12, Vol.78 (24), p.35633-35650
Main Authors: Ding, Lu, Wang, Yong, Laganière, Robert, Luo, Xinbin, Fu, Shan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Computer Communication Networks
Computer engineering
Computer Science
Data Structures and Information Theory
Datasets
Electrical engineering
Hands
Intelligent vehicles
Localization
Methods
Multimedia
Multimedia Information Systems
Proposals
Special Purpose and Application-Based Systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we present a multi-scale predictions fusion region-based Fully Convolutional Networks (MSPF-RFCN) to robustly detect and classify human hands under various challenging conditions. In our approach, the input image is passed through the proposed network to generate score maps, based on multi-scale predictions fusion. The network has been specifically designed to deal with small objects. It uses an architecture based on region proposals generated at multiple scales. Our method is evaluated on challenging hand datasets, namely the Vision for Intelligent Vehicles and Applications (VIVA) Challenge and the Oxford hand dataset. It is compared against recent hand detection algorithms. The experimental results demonstrate that our proposed method achieves state-of-the-art detection for hands of various sizes.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-08080-4