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...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 78; no. 24; pp. 35633 - 35650
Main Authors Ding, Lu, Wang, Yong, Laganière, Robert, Luo, Xinbin, Fu, Shan
Format Journal Article
LanguageEnglish
Published New York Springer US 01.12.2019
Springer Nature B.V
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
Deep learning
Region proposal network
Hand detection
Fully convolutional network
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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